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One Click: Jeff Bezos and the Rise of Amazon.com by Richard L. Brandt
Amazon Web Services, automated trading system, big-box store, call centre, cloud computing, Dynabook, Elon Musk, inventory management, Jeff Bezos, Kevin Kelly, new economy, science of happiness, search inside the book, Silicon Valley, Silicon Valley startup, skunkworks, software patent, Steve Jobs, Stewart Brand, Tony Hsieh, Whole Earth Catalog, Y2K
Netflix can’t afford (at least not yet) to buy all the computing power needed to load up films instantly and stream them to thousands of customers at any moment. So it rents computers from Amazon’s vast store at pennies per minute to handle the tasks, tapping into just as much computer power as it needs at any given moment. It’s all part of a surprising business from the online retailing company, called Amazon Web Services, which is part of a larger trend known as cloud computing. Services like this bring in half a billion dollars annually in revenues to Amazon. Buying companies is a relatively easy way for a stock-rich company to expand its business. But sometimes a great executive will stumble upon an unexpected new idea, or one of his employees may come up with something. The key is the ability to look beyond the current conventional wisdom and embrace a radical new idea.
That prevented sensitive data from leaking out. Despite some concerns from higher-ups about security, Frederick recalled, “The funny thing is that it did not take a great deal of convincing.” The executives soon realized that they could have a gold mine in their cubicles. By making its data and tools available to outside programmers, Amazon could actually outsource the development of new products—for free. Amazon Web Services was launched in July 2002. “We’re putting out a welcome mat for developers,” announced Bezos. “This is an important beginning and new direction for us.” Developers began creating new sites with original features that could send new buyers to Amazon and help them find and buy products. One former Amazon developer, for example, created a site that he dubbed “Amazon Light.” It included a search box to find any product for sale at Amazon and sported the company’s “Buy” button, but added a feature.
What was this, the Macy’s Santa from Miracle on 34th Street? No, but it was a great feature that benefitted customers. Besides, if the customers did decide to buy from the outside site, the transaction actually went through Amazon, which collects a fee for the transaction. Site owners who sent customers to Amazon to buy Amazon products got about a 15 percent cut of any sale they sent to Amazon. Web services—the process of sharing services between Web sites—had been discussed by Internet pundits for years. Amazon was the first to make the concept a reality in a big way. About two years after the launch of Web services, Amazon boasted sixty-five thousand developers using the program and sending some ten million queries a day to Amazon’s servers.That’s a lot of new customers. Further, the offering began to look a lot like the phenomenon now known as cloud computing—tapping into a program sitting on a Web server somewhere rather than one sitting on your own desktop.
The New Kingmakers by Stephen O'Grady
Amazon Web Services, barriers to entry, cloud computing, correlation does not imply causation, crowdsourcing, DevOps, Jeff Bezos, Khan Academy, Kickstarter, Mark Zuckerberg, Netflix Prize, Paul Graham, Silicon Valley, Skype, software as a service, software is eating the world, Steve Ballmer, Steve Jobs, Tim Cook: Apple, Y Combinator
Even with a growing portfolio of high-quality open source software available to them, developers remained limited by the availability of hardware. As creative as they could now be with their software infrastructure, to build anything of size, they would eventually have to procure hardware. This meant either purchasing it outright or renting it, typically for the minimum of a month, with the attendant set up, management, and maintenance fees on top. Enter Amazon Web Services (AWS). The idea was simple. Driven relentlessly by Moore’s Law, hardware doubled in speed every two years. Like Google and other Internet giants, Amazon discovered early that the most-economical model for scaling its technology was on cheap, commodity servers deployed by the hundreds or thousands. Having acquired the expertise to build, run, and manage these machines at scale, Amazon would leverage the same as a product.
Fielding, one the authors of HTTP, the protocol that still powers the Internet today—you’ve probably typed http:// many times yourself—advocated for a simple style that both reflected and leveraged the way the Internet itself had been constructed. Unsurprisingly, this simpler approach proved popular. At Amazon, for example, developers were able to choose between the two different mechanisms to access data: SOAP or REST. Even in 2004, 80% of those leveraging Amazon Web Services did so via REST. Two years after that, Google deprecated their SOAP API for search. And they were just the beginning. It’s hard to feel too sorry for SOAP’s creators, however—particularly if what one Microsoft developer told Tim O’Reilly is true: “It was actually a Microsoft objective to make [SOAP] sufficiently complex that only the tools would read and write this stuff,” he explained, “and not humans.”
Developers are attracted to its platform because of the size of the market…those developers create thousands of new applications…the new applications give consumers thousands of additional reasons to buy Apple devices rather than the competition…and those new Apple customers give even more developers reason to favor Apple. Apple not only profits from this virtuous cycle, it benefits from ever-increasing economies of scale, realizing lower component costs than competitors. None of which would be possible without the developers Apple has recruited and, generally, retained. Amazon Web Services The company that started the cloud computing craze was founded in 1994 as a bookstore. The quintessential Internet company, Amazon.com competed with the traditional brick-and-mortar model via an ever-expanding array of technical innovations: some brilliant, others mundane. The most-important of Amazon’s retail innovations co-opted its customers into contributors. From affiliate marketing programs to online reviews, Amazon used technology to enable its customers’ latent desire to more fully participate in the buying process.
The Everything Store: Jeff Bezos and the Age of Amazon by Brad Stone
3D printing, airport security, AltaVista, Amazon Mechanical Turk, Amazon Web Services, bank run, Bernie Madoff, big-box store, Black Swan, book scanning, Brewster Kahle, call centre, centre right, Clayton Christensen, cloud computing, collapse of Lehman Brothers, crowdsourcing, cuban missile crisis, Danny Hillis, Douglas Hofstadter, Elon Musk, facts on the ground, game design, housing crisis, invention of movable type, inventory management, James Dyson, Jeff Bezos, Kevin Kelly, Kodak vs Instagram, late fees, loose coupling, low skilled workers, Maui Hawaii, Menlo Park, Network effects, new economy, optical character recognition, pets.com, Ponzi scheme, quantitative hedge fund, recommendation engine, Renaissance Technologies, RFID, Rodney Brooks, search inside the book, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, Skype, statistical arbitrage, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Thomas L Friedman, Tony Hsieh, Whole Earth Catalog, why are manhole covers round?
Bezos himself bought into the Web’s new orthodoxy of openness, preaching inside Amazon over the next few months that they should make these new tools available to developers and “let them surprise us.” The company held its first developer conference that spring and invited all the outsiders who were trying to hack Amazon’s systems. Now developers became another constituency at Amazon, joining customers and third-party sellers. And the new group, run by Colin Bryar and Rob Frederick, was given a formal name: Amazon Web Services. It was the trailhead of an extremely serendipitous path. Amazon Web Services, or AWS, is today in the business of selling basic computer infrastructure like storage, databases, and raw computing power. The service is woven into the fabric of daily life in Silicon Valley and the broader technology community. Startups like Pinterest and Instagram rent space and cycles on Amazon’s computers and run their operations over the Internet as if the high-powered servers were sitting in the backs of their own offices.
Various divisions of the U.S. government, such as NASA and the Central Intelligence Agency, are high-profile AWS customers as well. Though Amazon keeps AWS’s financial performance and profitability a secret, analysts at Morgan Stanley estimate that in 2012, it brought in $2.2 billion in revenue. The rise of Amazon Web Services brings up a few obvious questions. How did an online retailer spawn such a completely unrelated business? How did the creature that was originally called Amazon Web Services—the group working on the commerce APIs—evolve into something so radically different, a seller of high-tech infrastructure? Early observers suggested that Amazon’s retail business was so seasonal—booming during the holiday months—that Bezos had decided to rent his spare computer capacity during the quieter periods.
Chapter 7: A Technology Company, Not a Retailer 1 Gary Rivlin, “A Retail Revolution Turns Ten,” New York Times, July 27, 2012. 2 Gary Wolf, “The Great Library of Amazonia,” Wired, October 23, 2003. 3 Ibid. 4 Luke Timmerman, “Amazon’s Top Techie, Werner Vogels, on How Web Services Follows the Retail Playbook,” Xconomy, September 29, 2010. 5 Shobha Warrier, “From Studying under the Streetlights to CEO of a U.S. Firm!,” Rediff, September 1, 2010. 6 Tim O’Reilly, “Amazon Web Services API,” July 18, 2002, http://www.oreillynet.com/pub/wlg/1707. 7 Damien Cave, “Losing the War on Patents,” Salon, February 15, 2002. 8 O’Reilly, “Amazon Web Services API.” 9 Steve Grand, Creation: Life and How to Make It (Darby, PA: Diane Publishing, 2000), 132. 10 Hybrid machine/human computing arrangement patent filed October 12, 2001; http://www.google.com/patents/US7197459. 11 “Artificial Artificial Intelligence,” Economist, June 10, 2006. 12 Katharine Mieszkowski, “I Make $1.45 a Week and I Love It,” Salon, July 24, 2006. 13 Jason Pontin, “Artificial Intelligence, with Help from the Humans,” New York Times, March 25, 2007. 14 Jeff Bezos, interview by Charlie Rose, Charlie Rose, PBS, February 26, 2009.
Python for Unix and Linux System Administration by Noah Gift, Jeremy M. Jones
Amazon Web Services, bash_history, cloud computing, create, read, update, delete, database schema, Debian, distributed revision control, Firefox, industrial robot, inventory management, job automation, MVC pattern, skunkworks, web application
Bayer, Michael, SQLAlchemy ORM Bicking, Ian, virtualenv blocks of code, editing, Magic Edit bookmark command, bookmark bookmarks, cd navigating to bookmarked directories, cd bootstrapped virtual environment, custom, Creating a Custom Bootstrapped Virtual Environment Boto (Amazon Web services), Amazon Web Services with Boto Buildout tool, Buildout, Developing with Buildout, Developing with Buildout developing with, Developing with Buildout bzip2 compression, Using tarfile Module to Create TAR Archives C callbacks, Callbacks, Callbacks capitalization, Built-in methods for str data extraction (see case) case (capitalization), Built-in methods for str data extraction converting for entire string, Built-in methods for str data extraction cd command, cd, cd, cd, dhist -<TAB> option, dhist -b option, cd charts, creating, Graphical Images checksum comparisons, MD5 Checksum Comparisons, MD5 Checksum Comparisons choices usage pattern (optparse), Choices Usage Pattern close( ) function (socket module), socket close() method, Creating files close() method (shelve), shelve cloud computing, Cloud Computing, Building a sample Google App Engine application, Amazon Web Services with Boto, Google App Engine, Building a sample Google App Engine application Amazon Web services with Boto, Amazon Web Services with Boto cmp() function (filecmp module), Using the filecmp Module combining strings, Built-in methods for str data extraction command history, History command line, Introduction, Summary, Basic Standard Input Usage, Basic Standard Input Usage, Introduction to Optparse, Option with Multiple Arguments Usage Pattern, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes, Integrating Configuration Files, Integrating Configuration Files basic standard input usage, Basic Standard Input Usage, Basic Standard Input Usage integrating configuration files, Integrating Configuration Files, Integrating Configuration Files integrating shell commands, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes optparse, Introduction to Optparse, Option with Multiple Arguments Usage Pattern community of Python users, Why Python?
to obtain object information, pinfo ' (quotation mark, single), Creating strings creating strings with, Creating strings " (quotation marks, double), Creating strings creating strings with, Creating strings _ (underscore), rehash, psearch, psearch, History results, History results for results history, History results, History results __ (in variable names), rehash __ object, psearch ___ object, psearch “magic” functions, alias (see also specific function) A Active Directory, using with Python, Using LDAP with OpenLDAP, Active Directory, and More with Python, Importing an LDIF File active version of package, changing, Change Active Version of Package alias function, alias, alias, alias alias table, rehash, rehashx Amazon Web services (Boto), Amazon Web Services with Boto Apache config file, hacking (example), Apache Config File Hacking, Apache Config File Hacking Apache log reporting, Apache Log Reporting, Apache Log Reporting Apache Log Viewer, building (example), Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using Curses, Building an Apache Log Viewer Using Curses, Apache Log Viewer Application, Apache Log Viewer Application with curses library, Building an Apache Log Viewer Using Curses, Building an Apache Log Viewer Using Curses with Django, Apache Log Viewer Application, Apache Log Viewer Application with PyGTK, Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using PyGTK Apache logfile, parsing (example), Log Parsing, Log Parsing appscript project, OS X Scripting APIs archiving data, Archiving, Compressing, Imaging, and Restoring, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files examining TAR file contents, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files ARP protocol, Creating Hybrid SNMP Tools asr utility, Automatically Re-Imaging Machines attachments (email), sending, Sending attachments with Python attrib attribute (Element object), ElementTree authentication, Authenticating to a Password Protected Site when installing eggs, Authenticating to a Password Protected Site authentication (SMTP), Using SMTP authentication automated information gathering, Automated Information Gathering, Receiving Email, Receiving Email, Receiving Email receiving email, Receiving Email, Receiving Email automatically re-imaging routines, Automatically Re-Imaging Machines automation, with IPython shell, Automation and Shortcuts, rep B background threading, IPython and Net-SNMP backslash (\), Creating strings escape sequences, list of, Creating strings backups, Introduction, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files examining TAR file contents, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files bar charts, creating, Graphical Images Bash, Python versus, Why Python?
to obtain object information, pinfo ' (quotation mark, single), Creating strings creating strings with, Creating strings " (quotation marks, double), Creating strings creating strings with, Creating strings _ (underscore), rehash, psearch, psearch, History results, History results for results history, History results, History results __ (in variable names), rehash __ object, psearch ___ object, psearch “magic” functions, alias (see also specific function) A Active Directory, using with Python, Using LDAP with OpenLDAP, Active Directory, and More with Python, Importing an LDIF File active version of package, changing, Change Active Version of Package alias function, alias, alias, alias alias table, rehash, rehashx Amazon Web services (Boto), Amazon Web Services with Boto Apache config file, hacking (example), Apache Config File Hacking, Apache Config File Hacking Apache log reporting, Apache Log Reporting, Apache Log Reporting Apache Log Viewer, building (example), Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using Curses, Building an Apache Log Viewer Using Curses, Apache Log Viewer Application, Apache Log Viewer Application with curses library, Building an Apache Log Viewer Using Curses, Building an Apache Log Viewer Using Curses with Django, Apache Log Viewer Application, Apache Log Viewer Application with PyGTK, Building an Apache Log Viewer Using PyGTK, Building an Apache Log Viewer Using PyGTK Apache logfile, parsing (example), Log Parsing, Log Parsing appscript project, OS X Scripting APIs archiving data, Archiving, Compressing, Imaging, and Restoring, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files examining TAR file contents, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files ARP protocol, Creating Hybrid SNMP Tools asr utility, Automatically Re-Imaging Machines attachments (email), sending, Sending attachments with Python attrib attribute (Element object), ElementTree authentication, Authenticating to a Password Protected Site when installing eggs, Authenticating to a Password Protected Site authentication (SMTP), Using SMTP authentication automated information gathering, Automated Information Gathering, Receiving Email, Receiving Email, Receiving Email receiving email, Receiving Email, Receiving Email automatically re-imaging routines, Automatically Re-Imaging Machines automation, with IPython shell, Automation and Shortcuts, rep B background threading, IPython and Net-SNMP backslash (\), Creating strings escape sequences, list of, Creating strings backups, Introduction, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files examining TAR file contents, Using a tarfile Module to Examine the Contents of TAR Files, Using a tarfile Module to Examine the Contents of TAR Files bar charts, creating, Graphical Images Bash, Python versus, Why Python? Bayer, Michael, SQLAlchemy ORM Bicking, Ian, virtualenv blocks of code, editing, Magic Edit bookmark command, bookmark bookmarks, cd navigating to bookmarked directories, cd bootstrapped virtual environment, custom, Creating a Custom Bootstrapped Virtual Environment Boto (Amazon Web services), Amazon Web Services with Boto Buildout tool, Buildout, Developing with Buildout, Developing with Buildout developing with, Developing with Buildout bzip2 compression, Using tarfile Module to Create TAR Archives C callbacks, Callbacks, Callbacks capitalization, Built-in methods for str data extraction (see case) case (capitalization), Built-in methods for str data extraction converting for entire string, Built-in methods for str data extraction cd command, cd, cd, cd, dhist -<TAB> option, dhist -b option, cd charts, creating, Graphical Images checksum comparisons, MD5 Checksum Comparisons, MD5 Checksum Comparisons choices usage pattern (optparse), Choices Usage Pattern close( ) function (socket module), socket close() method, Creating files close() method (shelve), shelve cloud computing, Cloud Computing, Building a sample Google App Engine application, Amazon Web Services with Boto, Google App Engine, Building a sample Google App Engine application Amazon Web services with Boto, Amazon Web Services with Boto cmp() function (filecmp module), Using the filecmp Module combining strings, Built-in methods for str data extraction command history, History command line, Introduction, Summary, Basic Standard Input Usage, Basic Standard Input Usage, Introduction to Optparse, Option with Multiple Arguments Usage Pattern, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes, Integrating Configuration Files, Integrating Configuration Files basic standard input usage, Basic Standard Input Usage, Basic Standard Input Usage integrating configuration files, Integrating Configuration Files, Integrating Configuration Files integrating shell commands, Unix Mashups: Integrating Shell Commands into Python Command-Line Tools, Hybrid Kudzu Design Pattern: Wrapping a Unix Tool in Python to Spawn Processes optparse, Introduction to Optparse, Option with Multiple Arguments Usage Pattern community of Python users, Why Python?
Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You by Sangeet Paul Choudary, Marshall W. van Alstyne, Geoffrey G. Parker
3D printing, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, Apple's 1984 Super Bowl advert, autonomous vehicles, barriers to entry, big data - Walmart - Pop Tarts, bitcoin, blockchain, business process, buy low sell high, chief data officer, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, discounted cash flows, disintermediation, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, Haber-Bosch Process, High speed trading, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, Khan Academy, Kickstarter, Lean Startup, Lyft, market design, multi-sided market, Network effects, new economy, payday loans, peer-to-peer lending, Peter Thiel, pets.com, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Satoshi Nakamoto, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Skype, smart contracts, smart grid, Snapchat, software is eating the world, Steve Jobs, TaskRabbit, The Chicago School, the payments system, Tim Cook: Apple, transaction costs, two-sided market, Uber and Lyft, Uber for X, winner-take-all economy, Zipcar
Airbnb, “Host Protection Insurance,” https://www.airbnb.com/host-protection-insurance, accessed June 15, 2015; A. Cecil, “Uber, Lyft, and Other Rideshare Drivers Now Have Insurance Options,” Policy Genius, https://www.policygenius.com/blog/uber-lyft-and-other-rideshare-drivers-now-have-insurance-options/, accessed June 14, 2015. 48. Huckman, Pisano, and Kind, “Amazon Web Services.” 49. Jillian D’Onfro, “Here’s a Reminder Just How Massive Amazon’s Web Services Business Is,” Business Insider, June 16, 2014, http://www .businessinsider.com/amazon-web-services-market-share-2014-6. 50. Annabelle Gawer and Michael A. Cusumano, Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation (Boston: Harvard Business School Press, 2002). 51. Adapted from Gawer and Cusumano, Platform Leadership. 52. Clarkson and Van Alstyne, “The Social Efficiency of Fairness.” 53.
Second, a platform ecosystem can evolve faster when the core platform is a clean, simple system rather than a tangle of numerous features. For this reason, C. Y. Baldwin and K. B. Clark of Harvard Business School describe a well-designed platform as consisting of a stable core layer that restricts variety, sitting underneath an evolving layer that enables variety.12 Today’s best-designed platforms incorporate this structural principle. For example, Amazon Web Services (AWS), the most successful platform for providing cloud-based information storage and management, focuses on optimizing a handful of basic operations, including data storage, computation, and messaging.13 Other services, which are used by just a fraction of AWS customers, are restricted to the periphery of the platform and provided through purpose-built apps. THE POWER OF MODULARITY There are advantages to an integral approach where the system is developed as quickly as possible to serve a single purpose, especially in the early days of a platform.
Bezos doesn’t care. 5. All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions. 6. Anyone who doesn’t do this will be fired. 7. Thank you; have a nice day! Astute application of this principle of transparency underlies the success of Amazon Web Services (AWS), the platform’s giant cloud services company. Andrew Jassy, Amazon’s vice president of technology, had observed how different divisions of Amazon kept having to develop web service operations to store, search, and communicate data.48 Jassy urged that these varied projects should be combined into a single operation with one clear, flexible, and universally comprehensible set of protocols.
Deploying OpenStack by Ken Pepple
It is intended to provide the reader with a solid understanding of the OpenStack project goals, details of specific OpenStack software components, general design decisions, and detailed steps to deploy OpenStack in a few controlled scenarios. Along the way, readers would also learn common pitfalls in architecting, deploying, and implementing their cloud. Intended Audience This book assumes that the reader is familiar with public Infrastructure as a Service (IaaS) cloud offerings such as Rackspace Cloud or Amazon Web Services. In addition, it demands an understanding of Linux systems administration, such as installing servers, networking with iptables, and basic virtualization technologies. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.
# nova-manage db sync # To view the database scheme version, use the db version arguments: # nova-manage db version 14 Note The database version for Cactus is 14 Instance Types and Flavors Instance types (or “flavors,” as the OpenStack API calls them) are resources granted to instances in Nova. In more specific terms, this is the size of the instance (vCPUs, RAM, Storage, etc.) that will be launched. You may recognize these by the names “m1.large” or “m1.tiny” in Amazon Web Services EC2 parlance. The OpenStack API calls these “flavors” and they tend to have names like “256 MB Server.” Instance types or flavors are managed through nova-manage with the instance_types command and an appropriate subcommand. At the current time, instance type manipulation isn’t exposed through the APIs nor the adminclient. Note You can use the flavor command as a synonym for instance_types in any of these examples.
Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest
23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, ethereum blockchain, Galaxy Zoo, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loose coupling, loss aversion, Lyft, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, Network effects, new economy, Oculus Rift, offshore financial centre, p-value, PageRank, pattern recognition, Paul Graham, Peter H. Diamandis: Planetary Resources, Peter Thiel, prediction markets, profit motive, publish or perish, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, subscription business, supply-chain management, TaskRabbit, telepresence, telepresence robot, Tony Hsieh, transaction costs, Tyler Cowen: Great Stagnation, urban planning, WikiLeaks, winner-take-all economy, X Prize, Y Combinator
They have transformed the world of newspapers and publishing. And they have profoundly changed the way we communicate and interact with one another. One reason for that change is that the cost of distributing a product or service, particularly if can be converted almost entirely to information, has dropped almost to zero. It used to require millions of dollars in servers and software to launch a software company. Thanks to Amazon Web Services (AWS), it now costs just a tiny fraction of that amount. Similar stories can be found in every department in every industry of the modern economy. History and common sense make clear that you cannot radically transform every part of an organization—and accelerate the underlying clock of that enterprise to hyper-speed—without fundamentally changing the nature of that organization. Which is why, over the last few years, a new organizational scheme congruent with these changes has begun to emerge.
It is our belief that ExOs will overwhelm traditional linear organizations in most industries because they take better advantage of the information-based externalities inaccessible to older structures, a feat that will empower them to grow faster—shockingly faster—than their linear counterparts, and then accelerate from there. It’s hard to pin down exactly when this new organizational form emerged. Various aspects of ExOs have been around for decades, but it is only over the last few years that they have really started to matter. If we had to pick an official ExO origin date, it would be March 2006, when Amazon launched Amazon Web Services and created the low-cost “Cloud” for medium and small businesses. From that date on, the cost of running a data center moved from a fixed CAPEX (Capital Expenditure) cost to a variable cost. Today, it is almost impossible to find a single startup that doesn’t use AWS. We have even found a simple metric that helps to identify and distinguish emerging Exponential Organizations: a minimum 10x improvement in output over four to five years.
In the case of counterexamples—such as Tesla owning its own factories or Amazon owning its own warehouses and local delivery services—the underlying reason isn’t financial; instead, the driving force is the scarcity of mission-critical resources involved, or that it’s so new that it’s now fully fleshed out. The information age now enables Apple and other companies to access physical assets anytime and anywhere, rather than requiring that they actually possess them. Technology enables organizations to easily share and scale assets not only locally, but also globally, and without boundaries. As we noted earlier, the launch of Amazon Web Services in March 2006 was a key inflection point in the rise of ExOs. The ability to lease on-demand computing that would scale on a variable cost basis utterly changed the IT industry. A new Silicon Valley phenomenon called TechShop is another example of this trend. In the same way that gyms use a membership model to aggregate expensive exercise machinery that few could afford to have at home, TechShop collects expensive manufacturing machinery and offers subscribers a small monthly fee ($125 to $175, depending on the location) for unlimited access to its assets.
Airbnb, airport security, Albert Einstein, altcoin, Amazon Web Services, bitcoin, Black Swan, blockchain, business process, centralized clearinghouse, Clayton Christensen, cloud computing, cryptocurrency, disintermediation, distributed ledger, Edward Snowden, en.wikipedia.org, ethereum blockchain, fault tolerance, fiat currency, global value chain, Innovator's Dilemma, Internet of things, Kevin Kelly, Kickstarter, market clearing, Network effects, new economy, peer-to-peer lending, prediction markets, pull request, ride hailing / ride sharing, Satoshi Nakamoto, sharing economy, smart contracts, social web, software as a service, too big to fail, Turing complete, web application
More likely, the blockchain infrastructure resembles a layer of cloud computing infrastructure. Blockchain virtual machines may be too expensive if we are to literally compare their functionality to a typical cloud service such as Amazon Web Services or DigitalOcean, but they will be be certainly useful for smart contracts that execute their logic on the blockchain’s virtual machinery, or decentralized applications, also called Dapps. As a sidenote, we could also see a future where client nodes can talk to each other directly in scenarios where blockchains are too expensive or slow. When you run an application in the cloud (for example, on Amazon Web Services or Microsoft Azure), you are billed according to a combination of time, storage, data transfer, and computing speed requirements. The novelty with virtual machine costing is that you are paying to run the business logic on the blockchain, which is otherwise running on physical servers (on existing cloud infrastructure), but you do not have to worry about setting up these servers because they are managed by other users who are getting paid anyways for running that infrastructure via mining.
There is magic when you figure out the blockchain’s touch points to your business and you start offering new user experiences that didn’t exist before. These new areas will include banking without banks, gambling without the house’s edge, title transfers without central authorities stamping them, e-commerce without eBay, registrations without government officials overseeing them, computer storage without Dropbox, transportation services without Uber, computing without Amazon Web Services, online identities without Google, and that list will continue to grow. Take any services and add “without previous center-based authority,” and replace with “peer-to-peer, trust-based network,” and you will start to imagine the possibilities. The general characteristics of decentralization-based services include: Speed in settlements No intermediary delays Upfront identification and reputation Flat structure with no overhead Permission-less user access Trust built inside the network Resiliency against attacks No censorship No central point of failure Governance decisions by consensus Peer-to-peer communications THE CRYPTO ECONOMY What started as Bitcoin, the poster child cryptocurrency that captured our imagination, is leading to a multiplicity of blockchain-enabled businesses and implementations.
The Alliance: Managing Talent in the Networked Age by Reid Hoffman, Ben Casnocha, Chris Yeh
And that’s how Lasseter ended up back at Disney as its chief creative officer of Disney Animation Studios.9 Disney’s management hired an entrepreneurial talent like Lasseter, but they treated him as a commodity rather than an ally, and in the process, they lost their chance to develop a multibillion-dollar business. Lasseter would have been happy to develop that business within Disney, but his managers wouldn’t let him. Benjamin Black and Amazon Web Services Amazon didn’t make the same mistake as Disney. Recently, it used the principles of the alliance to generate a new multibillion-dollar business. Amazon has become a leader in the field of cloud computing, thanks to Amazon Web Services (AWS), which allows companies to rent online storage and computing power, rather than buying and operating their own servers. Companies ranging from Fortune 500 giants to one-person start-ups run their businesses on AWS. What most people don’t realize is that the idea for AWS didn’t come from Amazon’s famed entrepreneurial founder and CEO, Jeff Bezos, or even from a member of his executive team, but rather from an “ordinary” employee.
CTOs at Work by Scott Donaldson, Stanley Siegel, Gary Donaldson
Amazon Web Services, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, domain-specific language, glass ceiling, pattern recognition, Pluto: dwarf planet, Richard Feynman, Richard Feynman, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, thinkpad, web application, zero day
Index 3D technology David Kuttler, 302 Jerry Krill, 83–85 64-bit architectures, 272–274, 281 A acquisitions, William Ballard, 279–280 Adobe, reverse image searching and, 246 AEP (American Electric Power), 128–129 Aguru Images, Craig Miller, 53–54, 56–59 algae, Craig Miller, 69, 76 Alm, Al, 50 Alving, Amy, 1–20 Amazon EC2, 36 Amazon Web Services (AWS), 245 American Electric Power (AEP), 128–129 American Public Media, 163 Amperion, 128 APL (Applied Physics Laboratory) global technical outreach, 90 Jerry Krill, 82 application development, Jeff Tolnar, 145 architecture technical review, 26 Arduinos, Craig Miller, 77 asymmetric warfare, Jerry Krill's comments on, 92 AWS (Amazon Web Services), 245 B Ballard, William, CTO of Gerson Lehrman Group, Inc, 261–283 Beyster, Bob, 51–52 Bilger, Mark, 23 Black, David, 262 Bloore, Paul, 239–260 Bodine, Greg, 30 BPLG (BPL Global), 127–128, 132 breakout engagements, Gerson Lehrman, 265 budgeting, Jeff Tolnar, 146–148 bundling hardware and software, Jeff Tolnar, 135 Burke, Thomas, 177 business development personnel, Gerson Lehrman, 266 C CA Open Space, 39 callable interface, 41 career lessons Darko Hrelic, 206–207, 211 Jan-Erik de Boer, 236 Jerry Krill, 91 Paul Bloore, 253 Wesley Kaplow, 110 career path Amy Alving, 1–6 Darko Hrelic, 205–206 Dmitry Cherches, 181–185 Jan-Erik de Boer, 237 Jeff Tolnar, 128 Marty Garrison, 152 Paul Bloore, 240–242 Tom Loveland, 176–178 Wesley Kaplow, 105–110 CATEX, Craig Miller, 51 Cherches, Dimitri background, 173 of Mind Over Machines, 173–204 ChoicePoint, 158 CIO responsibilities, of William Ballard, 268 CIO role, versus CTO role, 282 CIOs, Jeff Tolnar, 130–131 cloud computing, 35 Amy Alving, 16–17 Craig Miller, 65 David Kuttler, 302 Dmitry Cherches, 186–188, 200 Gerson Lehrman, 277 Jeff Tolnar, 142, 147–148 Marty Garrison, 160, 167 CloudShield, 13 cluster analysis, Craig Miller, 64 CommonCrawl group, 257 communication activities, of William Ballard, 269 communication, importance of, 30 competition, evaluating Amy Alving, 12–13 Darko Hrelic, 211–212, 217 Gerson Lehrman, 265 Jerry Krill, 101–102 Paul Bloore, 244–245 computing cloud computing, 35 Amy Alving, 16–17 Craig Miller, 65 David Kuttler, 302 Dmitry Cherches, 186–188, 200 Gerson Lehrman, 277 Jeff Tolnar, 142, 147–148 Marty Garrison, 160, 167 distributed computing, 242 mobile computing, 39–40 Darko Hrelic, 209–210 impact on Gerson Lehrman, 276 Jerry Krill, 96 Marty Garrison, 168 Paul Bloore, 254 Wesley Kaplow, 123 Comverge, 137 conference calls, ZipDx, 277–278 consulting, by Gerson Lehrman, 265 continuous deployment cycle, at Gerson Lehrman, 281 corporate strategy, Rick Mosca, 191–194 councils, Gerson Lehrman, 264 creativity, role in career path Jerry Krill, 85–87 Paul Bloore, 241 Wesley Kaplow, 116 Crinks, Bert, 115–116 CTO responsibilities, of William Ballard, 267–271, 282–283 Cuomo, Jerry, 25 customizing software, William Ballard's advice regarding, 270–272 cyanobacteria, Craig Miller, 79 cyber security Amy Alving, 18–19 CloudShield, 13 Darko Hrelic, 214–215 Dmitry Cherches, 201 Gerson Lehrman, 276 Springer, 235 Wesley Kaplow, 123 D Dannelly, Doug, 69 DARPA (Defense Advanced Research Projects Agency), Amy Alving, 5–6 Darwin, Charles, 70 data mining, real-time, 281–282 databases Jeff Tolnar, 141 used at Gerson Lehrman, 272 David Kuttler, 296 DAVID system, 165 de Boer, Jan-Erik, 219–237 Debevec, Paul, 57 decision-making, Jeff Tolnar, 133–134 Defense Advanced Research Projects Agency (DARPA), Amy Alving, 5–6 Delman, Debra, 161 Demand Media, 261–262 Department of Energy (DoE), 54–55, 64 deployment cycle, at Gerson Lehrman, 281 DER (distributed energy resources), 139, 142–149 DEs (Distinguished Engineers), 25 development methodology, Paul Bloore, 256–257 development resources, Jeff Tolnar, 139–140 DiData, Craig Miller, 53, 61 digital watermarking, 243 Dimension Data, Craig Miller, 52 Distinguished Engineers (DEs), 25 distributed computing, 242 distributed energy resources (DER), 139, 142–149 Dobson, David, 36 DoE (Department of Energy), 54–55, 64 domain expertise, 36 E economics of consulting with Gerson Lehrman, 265 William Ballard's advice regarding, 270–271, 279 electric co-ops, Craig Miller, 54–55 electrical transformers, Craig Miller, 68 employees, Gerson Lehrman, 265–266 Endhiran movie, Craig Miller, 58–59 energy policy, Craig Miller, 50 engineering offices, Gerson Lehrman, 266 engineers, cost of, 270, 279 ETL (extraction, translation, load), 203 evaluating competition Amy Alving, 12–13 Darko Hrelic, 211–212, 217 Gerson Lehrman, 265 Jerry Krill, 101–102 Paul Bloore, 244–245 expert network space, Gerson Lehrman, 265 expertise, use of, 274–275 external organizations, affiliations of Gerson Lehrman with, 269 extinction, Craig Miller, 78–79 extraction, translation, load (ETL), 203 F FDA, David Kuttler, 300–301 Ferguson, Don, interview with, 21–47 Fernandez, Raul, 52 ferrofluid, 241 First of a Kind project, 23 Folderauer, Ken, 114 fractal architecture, 273–274 free space optics, 84, 95 frequency variations, Craig Miller, 62–63 functional programming, 278 fundraising, Jeff Tolnar, 146 G Garrison, Marty, interview with, 151 Gayal, Amoosh, 24 Gelpin, Tim, 89 G.I.
We now have over two billion images in our index, and a search takes about a second or so. So that in a nutshell is what TinEye is. It was something that I wanted us to do for quite some time. We released it in March of 2008, but I'd wanted to make a web-wide search for a couple of years prior to that, but we didn't actually have the computing capacity to do it. In late 2007, Amazon was developing their AWS, their Amazon Web Services, and cloud computing platform. I was immediately taken by it because I thought, “Aha. Now we can manage to get ahold of the computing capacity we need to launch the search engine.” So we started work on it immediately, and I think it was about four or five months later when we first put TinEye on the web. And, of course, that was about three years ago now. S. Donaldson: Right. So what's an application?
What's the world starting to look like, and how should we position our services so we take advantage of what the world's going to be in five years' time? One future vision I have is currently counter culture. One of the things that I'm kind of known for is that I do worry about costs. I'm kind of referred to as a cheap guy when it comes to spending money on equipment. So we started TinEye completely using cloud-based services on Amazon's Web Services. Pretty soon, though, we realized that we could do it much more cost-effectively on our own equipment. So this means that we moved away from cloud computing and we do everything ourselves in-house. And that's just interesting because it seems to be swimming against the stream as far as what I see when I speak with other technology people. Many start-ups I see are afraid of buying their own equipment.
Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley by Antonio Garcia Martinez
Airbnb, airport security, Amazon Web Services, Burning Man, Celtic Tiger, centralized clearinghouse, cognitive dissonance, collective bargaining, corporate governance, Credit Default Swap, crowdsourcing, death of newspapers, El Camino Real, Elon Musk, Emanuel Derman, financial independence, global supply chain, Goldman Sachs: Vampire Squid, hive mind, income inequality, interest rate swap, intermodal, Jeff Bezos, Malcom McLean invented shipping containers, Mark Zuckerberg, Maui Hawaii, means of production, Menlo Park, minimum viable product, move fast and break things, Network effects, Paul Graham, performance metric, Peter Thiel, Ponzi scheme, pre–internet, Ralph Waldo Emerson, random walk, Sand Hill Road, Scientific racism, second-price auction, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, social web, Socratic dialogue, Steve Jobs, telemarketer, urban renewal, Y Combinator, éminence grise
Until AdGrok’s very end, search terms like “goldman sachs” and “fuck you” (I had written a post about the ever-elusive goal of “fuck-you money”) would be the most popular terms that led to clicks to our site.* It irritated MRM to no end. But hey—I didn’t see fifty thousand people a day lining up to use the product we had built. We’d take eyeballs wherever we could find them. As a result of the Amazon Web Services near fiasco, plus several more outages and server meltdowns, MRM suggested we run a “chaos monkey” from time to time. This was a software tool created and open-sourced by Netflix, meant to test a product or website’s resiliency against random server failures (such as we’d just witnessed with the blog). In order to understand both the function and the name of the chaos monkey, imagine the following: a chimpanzee rampaging through a data center, one of the air-conditioned warehouses of blinking machines that power everything from Google to Facebook.
Angels who used to write $20,000 checks on a deal could now easily write one for $200,000 or more (e.g., our boy Sacca). In tandem, the popularity of accelerators like Y Combinator, plus a general acceptance of entrepreneurship as a career, meant lots of very skilled engineers and product people were skipping the corporate trajectory and building exciting products. The emergence of turnkey, on-demand computation like Amazon Web Services, plus off-the-shelf Web-development frameworks like Ruby on Rails, meant that new ideas were easier than ever to test. Many entrepreneurs chose to build shovels rather than dig for gold, creating more complex software building blocks to underpin the innovation, such as back-end services like Parse, accelerating the startup explosion in an almost exponential way. The net of all this change was that seed rounds were now reaching levels of former A rounds; a two-month-old company with a persuasive CEO raising $2 million and calling it a “seed” was not shocking news.
Taking both technical and legal forms, it’s the snooping around an acquiring company does to make sure it’s actually getting what it thinks it is. On the technical side, it means understanding the company’s “stack”; that is, the pile of interrelated user interface and back-end server technologies that power the product. It might even be as detailed as line-by-line code reviews with the startup’s engineers. You can fake a lot in a startup these days, what with Amazon Web Services and all sorts of off-the-shelf back-end components that let any even minimally competent duffer set up a Web app that does something. Intelligent planning for growth is rare among early startups, but it’s the name of the game at a large, rapidly scaling tech company. Waiting for a team to grow from technical adolescence to mature talent was too long even for a larger company. As a first step, Twitter had invited us in as a group to talk technical turkey with a pack of engineers that reported to Kevin Weil.
Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler
3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, cloud computing, crowdsourcing, Daniel Kahneman / Amos Tversky, dematerialisation, deskilling, Elon Musk, en.wikipedia.org, Exxon Valdez, fear of failure, Firefox, Galaxy Zoo, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, industrial robot, Internet of things, Jeff Bezos, John Harrison: Longitude, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, Mahatma Gandhi, Mark Zuckerberg, Mars Rover, meta analysis, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, telepresence, telepresence robot, Turing test, urban renewal, web application, X Prize, Y Combinator
• We will share our strategic thought processes with you when we make bold choices (to the extent competitive pressures allow), so that you may evaluate for yourselves whether we are making rational long-term leadership investments. • We will balance our focus on growth with emphasis on long-term profitability and capital management. At this stage, we choose to prioritize growth because we believe that scale is central to achieving the potential of our business model. This letter is often held up as the encapsulation of Bezos’s view on the subject, but personally, I think an answer he gave to an Amazon Web Services Live audience in 201227 was far more revealing: “What’s going to change in the next ten years?” And that is a very interesting question; it’s a very common one. I almost never get the question: “What’s not going to change in the next ten years?” And I submit to you that that second question is actually the more important of the two—because you can build a business strategy around the things that are stable in time. . . .
But it’s the combination of long-term thinking and customer-centrism that has helped Amazon extend their reach far beyond books. Bezos has ventured into music, movies, toys, electronics, automotive parts, and well, just about everything. They have also continued to surround their original market, moving from books into ebooks and ebook readers (with Kindle), and most recently, publishing itself. Meanwhile, Amazon Web Services—their cloud business—has become a beast in its own right (worth nearly $3 billion, according to a November 2013 Business Insider analysis).28 As Morgan Stanley analyst Scott Devitt told the New York Times:29 “Amazon is marching to a different drumbeat, which is long term. Are they doing the right thing? Absolutely. Amazon is growing at twice the rate of e-commerce as a whole, which is growing five times faster than retail over all.
Italic numbers refer to charts/graphs Aabar, 127 Abundance (Diamandis and Kotler), xi–xii, xv, 34, 54, 136, 137, 146, 162, 274 AbundanceHub.com, 158, 162, 210, 277 Abundance360Summit, 278 Academy of Achievement, 129 activists, xiii, 180 in crowdfunding campaigns, 201–3, 212, 230 additive manufacturing, 30, 31, 33, 41 AdhereTech, 47 AdSense, 139 Advanced Research Projects Agency Network (ARPANET), 27 advertising, 241, 242 in crowdfunding campaigns, 212–13 crowdsourcing platforms for, 151, 152–54, 158 advocates, in crowdfunding campaigns, 200–201, 205 AdWords, 241 aerospace industry, 112, 117, 133 skunk methodology used in, 71–73, 75 3–D printing and, 34, 35–37 see also space exploration affiliate marketing, 199–200, 205 Ahn, Luis von, 154, 155–56 Airbnb, 20, 21, 66 Airbus, 249 airlines, 43, 124, 125, 126, 127, 260 AI XPRIZE, 54 algorithms, 43, 51, 52, 66, 85, 220, 227 crowdsourcing projects and, 158, 159, 160, 161, 227 machine-learning, 54–55, 55, 58 Netflix Prize for, 254–56 PageRank, 135 Align Technology, 34–35 Amazon, 50, 76, 97, 128, 129–34, 157, 195, 254 drone proposal of, 61, 133–34 Amazon Web Services, 131, 132 America Online (AOL), 76, 143 Anderson, Chris, 10–11, 54, 123, 224, 229, 242 Anderson, Eric, 95, 96, 97, 98, 99, 100, 179, 202, 203–4 Andraka, Jack, 65 Andreessen, Marc, 27, 33 Android, 16, 135, 176 AngelList, 172, 173–74 Anheuser-Busch, 145 Ansari XPRIZE, 76, 96, 115, 127, 134, 246, 248–49, 253, 260, 261, 262, 263, 264, 265, 266, 267, 268 anti-aging projects, 66, 81, 136, 139 Apollo Program, 96, 100, 118, 139 Appert, Nicolas, 245 Appirio, 227–28 Apple, 18, 28, 62, 72, 111, 128 applications (apps), 13, 13, 15, 16, 28, 45, 47, 150, 158, 176 Arduino, 43 ARKYD Space Telescope campaign, 172, 174–75, 179–80, 186, 187, 188, 193, 195, 207, 209, 242 early donor engagement in, 203–5 hype created in, 205 launch of, 200, 208 recruiting of activists in, 201–3, 212 ”space selfie” reward offered in, 180, 189–90, 196, 208 Arnaout, Ramy, 227 artificial intelligence (AI), x, 22, 24, 41, 44, 52–59, 61, 62, 63, 66, 81, 135, 146, 160, 162, 216, 228, 275, 276, 295n crowdsourcing projects and, 167, 295n entrepreneurial opportunities in, 54, 56–59 Google’s development of, 24, 53, 58, 81, 138–39 Association of Space Explorers, 102 asteroids, ix–x, 180, 228–29 mining of, 95–96, 97–99, 107, 109, 179, 221, 276 Asteroid Zoo, 228 astronomy, 219–21, 228, 247, 267 Autodesk, 48–49, 51, 63, 65 automation, 47–48, 56 automobile industry, 29, 222–23 3–D printing in, 32 see also Local Motors; Tesla Motors autonomous cars, 43–44, 44, 48, 62, 66, 135, 136, 137, 262 autonomy, 79, 80, 85, 87, 92 Babson School of Business, 14 BackRub (research project), 135 Bacon, Jono, 237 Bad Girl Ventures, 19 bake-offs, in incentive competitions, 264 Barnett, Chance, 173 Barrie, Matt, 149–50, 158, 163, 165, 166, 167, 207 Barry, Dan, 35, 61, 62 Bass, Carl, 48–49, 50, 51 Baxter (robot), 60–61 Beland, Francis, 250 Bennett, Jim, 255 Berns, Gregory, 108 Beth Israel Deaconess Medical Center (Boston), 227 Better Blocks, 240–41 Bezos, Jeff, xiii, 73, 97, 115, 126, 128–34, 138, 139, 167 on risk management, 76–77 thinking-at-scale strategies of, 128, 129, 130–33 Bezos, Mark, 128 Bianchini, Gina, 217, 219, 224, 233 Big Think, 49, 121 biotechnology, 63–65 see also genomics; synthetic biology BlackBerry, 176 Blakey, Marion, 110 Blastar (video game), 117 blogs, in crowdfunding campaigns, 177, 205, 206 blue ellipticals, 219–20 Blue Origin, 97, 133 Boeing, 127, 249 Boston Dynamics, 61 Boston Globe, 227 Brand, Stewart, 26 Branson, Richard, xiii, 73, 84, 86, 99, 100, 111–12, 115, 123–28, 138, 139 space tourism projects of, 96–97, 115, 125, 127 thinking-at-scale strategies of, 125–27 Briggs, William, 47 Brin, Sergey, 81, 128, 135 British Admiralty, 267 British Airways, 124, 125, 126 British Medical Journal, 109 British Petroleum (BP), 250, 251 Brooks, Rodney A., 60 Brown, Dan, 152 brute force, 51 Burchard, Brendon, 210 Business Insider, 132 Business World, 144 buzz marketing, 240–41 Bye, Stephen, 45 Calacanis, Jason, 139 Calico, 139 Callaghan, Jon, 62 Caltech, 27 camel racing, 59–60 cameras, 3–4, 152 see also digital cameras Cameron, James, 250 campaign managers, in crowdfunding, 192, 194 Canadian Space Agency (CSA), 102 Cane, Daniel, 57 Capp, Al, 71, 72 CAPTCHA, 154, 155, 167, 295n CastingWords, 145 celebrity (the face): in crowdfunding campaigns, 192, 198, 207 in incentive competitions, 273 cell phones, 49, 135, 163 see also smartphones CFM International, 34 challenge/skills ratio, 91 “charge-coupled device” (CCD), 4–5 Chen, Michael, 35, 36, 37 China, 17, 18, 62 Chinese National Space Administration, 102 Chrome, 135, 138 Chung, Anshe, 144 Cinematch, 254, 255 Cisco, 46 Clarke, Arthur C., 52, 53, 100 cloud services, 39, 45, 50, 51, 56, 57, 63, 65, 66, 132, 216, 227–28 CNN, 48 cognitive biases, 246 cognitive surplus, 215 CoheroHealth, 47 Colgate Palmolive, 154 Comedy Central, 95 communities, online, 22, 182, 215–42, 243 building member base in early days of, 233–34 case studies of, 219–28 collaborative structures of, 217, 227, 228, 236, 237, 255 contests and competitions in building of, 224, 225–27, 232, 237, 240; see also incentive competitions DIY, see DIY communities driving growth in, 239–41 engagement strategies in, 224, 227, 235, 236–38, 239, 241 exponential, see exponential communities Law of Niches in, 221, 223, 228, 231 managing of, 238–39 monetization of, 241–42 passion as important in, 224, 225, 228, 231, 258 rate of innovation in, 216, 219, 224, 225, 228, 233, 237 rating systems in, 226, 232, 236–37 reputation economics in building of, 217–19, 230, 232, 236–37 self-organizing structures of, 217, 237 see also crowdfunding, crowdfunding campaigns; crowdsourcing Compaq, 117 computers, x, 7, 26, 72, 76, 135 see also artificial intelligence (AI); supercomputers Comsat, 102 constraints, power of, 248–49, 259 contract research and manufacturing services (CRAMS), 65 Coolest Cooler campaign, 210–13 corporate sponsorship, 246, 246 Cotichini, Christian, 257 Cotteleer, Mark, 33 Coulson, Simon, 150 Craigslist, 11, 257 creative assets, crowdsourcing of, 158 Creative Commons license, 224 Credit Suisse, 56 Cretaceous Period, ix CrossFit, 229 Crowdfunder, 172, 173, 175 crowdfunding, crowdfunding campaigns, xiii, 22, 103, 144–45, 147–48, 167, 169–213, 216, 242, 243, 247, 258, 270 advertising in, 212–13 building perfect team for, 191–94 building your audience in, 199–203 case studies in, 174–80 celebrity face of, 192, 198, 207 choosing idea for, 184–85 costs in, 195 data-driven decision making in, 207–10, 213 emergence of, 170–71, 170 engagement strategies in, 203–6, 207 feedback in, 176, 180, 182, 185, 190, 199, 200, 202, 209–10 fundraising targets in, 185–87, 191 global focus in, 209 how-to guide to, 181–213 launching with super-credibility in, 190, 199, 203, 204 length and schedule for, 187–89 pitch videos in, 177, 180, 192, 193, 195, 198–99, 203, 212 planning, materials, and resources in, 194–95 promotions and contests in, 207 reward-based, see reward-based crowdfunding setting rewards in, 189–91, 189 seven benefits of, 181–83 telling meaningful story in, 195–98 types of, 172–75 week-by-week execution plan for, 206–7 see also specific crowdfunding campaigns Crowdsortium, 162–63 crowdsourcing, xiii, 18, 22, 57, 85, 103, 143–67, 193, 223, 237, 240–41, 243, 245, 256, 275 in advertising, 151, 152–54, 158 AI as potential threat to, 167, 295n in automotive production, 223–24, 238 best practices of, 163–67 building communities for, see communities, online clear roles and communication in, 165–66 collaboration in, 144, 165–66, 167, 217, 227, 228, 236, 237, 255, 260–61 competitions and, 148, 152–54, 159, 160, 223, 224, 226–27, 232, 237, 240, 259; see also incentive competitions of creative and operational assets, 158–60 definition of, 144 in designing incentive competitions, 257–58 dual-use, 154–56 Freelancer.com case study in, 149–51, 158 growing interconnectedness and, 146–47, 147 incentive competitions and, see incentive competitions industry websites on, 162–63 of micro- vs. macrotasks, 156–58 most common uses for, 156–67 in product development, 18, 19, 223–25, 226–27 in retail and consumer products industry, 159–60 in scientific research, 145–46, 220–21, 227, 228–29 in software development, 144, 159, 161, 226–27, 236 of testing and discovery insights, 160–62 traffic data garnered by, 47 Crowdsourcing.org, 162 Csikszentmihalyi, Mihaly, 89, 92 Cube, 32 CubeSats, 36–37 Culver, Irv, 72 Cummins Engine, 222 Curiosity rover, 99 customer-centric business, 84, 116, 126, 128, 130, 131–32, 133, 138 Daily Show, 95 DARPA Grand Challenge, 262 Dartmouth Summer Research Project, 59 data mining, 42–44, 47–48, 256 AI’s role in, 55–59 behavior tracking and, 47 see also information data sets, preparing of, 164 Da Vinci Code, The (Brown), 152 debt funding, 172, 173, 174 deceptive phase, exponential, 8, 8, 9, 10, 24, 25–26, 29, 41 of AI, 59 in robotics, 60 of 3–D printing, 30, 31 Deep Learning (algorithm), 58, 59 Deepwater Horizon oil rig, 250 Defense Department, US, 71, 72 DeHart, Jacob, 143, 144 DeJulio, James, 151–52, 153, 166 Dell, 50 Deloitte Center for the Edge, 106 Deloitte Consulting, 33, 39, 159, 160, 245, 274 Deloitte University Press, 56 dematerialization, exponential, 8, 8, 10, 11–13, 14, 15, 20–21, 66 democratization, exponential, xii, 8, 10, 13–15, 21, 33, 59, 276 in bioengineering, 64–65 infinite computing and, 51–52 demonetization, exponential, 8, 8, 10–11, 14, 15, 138, 163, 167, 223 in bioengineering, 64–65 infinite computing and, 52 D.
Building Microservices by Sam Newman
airport security, Amazon Web Services, anti-pattern, business process, call centre, continuous integration, create, read, update, delete, defense in depth, Edward Snowden, fault tolerance, index card, information retrieval, Infrastructure as a Service, inventory management, job automation, load shedding, loose coupling, platform as a service, premature optimization, pull request, recommendation engine, social graph, software as a service, the built environment, web application, WebSocket, x509 certificate
You can target scaling at just those microservices that need it Gilt, an online fashion retailer, adopted microservices for this exact reason. Starting in 2007 with a monolithic Rails application, by 2009 Gilt’s system was unable to cope with the load being placed on it. By splitting out core parts of its system, Gilt was better able to deal with its traffic spikes, and today has over 450 microservices, each one running on multiple separate machines. When embracing on-demand provisioning systems like those provided by Amazon Web Services, we can even apply this scaling on demand for those pieces that need it. This allows us to control our costs more effectively. It’s not often that an architectural approach can be so closely correlated to an almost immediate cost savings. Ease of Deployment A one-line change to a million-line-long monolithic application requires the whole application to be deployed in order to release the change.
The problem, of course, is that if the same people create both the server API and the client API, there is the danger that logic that should exist on the server starts leaking into the client. I should know: I’ve done this myself. The more logic that creeps into the client library, the more cohesion starts to break down, and you find yourself having to change multiple clients to roll out fixes to your server. You also limit technology choices, especially if you mandate that the client library has to be used. A model for client libraries I like is the one for Amazon Web Services (AWS). The underlying SOAP or REST web service calls can be made directly, but everyone ends up using just one of the various software development kits (SDKs) that exist, which provide abstractions over the underlying API. These SDKs, though, are written by the community or AWS people other than those who work on the API itself. This degree of separation seems to work, and avoids some of the pitfalls of client libraries.
It wanted teams to own and operate the systems they looked after, managing the entire lifecycle. But Amazon also knew that small teams can work faster than large teams. This led famously to its two-pizza teams, where no team should be so big that it could not be fed with two pizzas. This driver for small teams owning the whole lifecycle of their services is a major reason why Amazon developed Amazon Web Services. It needed to create the tooling to allow its teams to be self-sufficient. Netflix learned from this example, and ensured that from the beginning it structured itself around small, independent teams, so that the services they created would also be independent from each other. This ensured that the architecture of the system was optimized for speed of change. Effectively, Netflix designed the organizational structure for the system architecture it wanted.
Remix: Making Art and Commerce Thrive in the Hybrid Economy by Lawrence Lessig
Amazon Web Services, Andrew Keen, Benjamin Mako Hill, Berlin Wall, Bernie Sanders, Brewster Kahle, Cass Sunstein, collaborative editing, disintermediation, don't be evil, Erik Brynjolfsson, Internet Archive, invisible hand, Jeff Bezos, jimmy wales, Kevin Kelly, late fees, Netflix Prize, Network effects, new economy, optical character recognition, PageRank, recommendation engine, revision control, Richard Stallman, Ronald Coase, Saturday Night Live, SETI@home, sharing economy, Silicon Valley, Skype, slashdot, Steve Jobs, The Nature of the Firm, thinkpad, transaction costs, VA Linux
In January 2007 the company began Amapedia, “a collaborative wiki for user-generated content related to ‘the products you like the most.’ ”10 In the decade since Amazon launched, it has delivered to the market an extraordinary range of innovation. Everything it does is aimed to drive sales of its products more efficiently. One of the techniques that Amazon uses mirrors the technique of the Internet generally: Amazon has opened its platform to allow others to innovate in new ways to build value out of Amazon’s database. Through a suite of tools called Amazon Web Services (AWS), Amazon enables developers to build products that integrate directly into Amazon’s database. For example, a developer named Jim Biancolo used AWS to build a free Web tool to track the price difference between new products and used products (plus shipping). And a company called TouchGraph used AWS to build a product browser that would show the links between related products. Enter Cass Sunstein’s, for example, and you’ll see all the books in Amazon that relate to Sunstein’s books in subject and citation.
Its purpose is to “improve the accuracy of predictions about how much someone is going to love a movie based on their movie preferences.”27 To achieve this end, Netflix runs a “Netflix Prize”—offering a grand prize of $1 million to anyone who improves Netflix’s own system by more than 10 percent. To enable this competition to happen, Netflix shared “a lot of anonymous rating data.” The company 80706 i-xxiv 001-328 r4nk.indd 137 8/12/08 1:55:20 AM REMI X 138 also increasingly offers through RSS feeds access to ranking information about its users’ choices. Amazon does this through its Amazon Web Services. And Google does this perhaps most of all, through Google APIs that encourage what has come to be known as the Google mash-up. Don Tapscott and Anthony Williams describe one example of the Google mash-up in their book, Wikinomics. In May 2005, Paul Rademacher was trying to find a house in Silicon Valley for his job at Dreamworks Animation. He grew weary of the piles of Google maps for each and every house he wanted to see, so he created a new Web site that cleverly combines listings from the online classified-ad service craigslist with Google’s mapping service.
., 195. 80706 i-xxiv 001-328 r4nk.indd 318 8/12/08 1:56:29 AM INDEX ABC, 213 Academy Awards, 45–46 access, 43–49, 67, 106, 255, 261, 291 advertising, 2, 48–49 blogs and, 63, 291 classified, 187 Dogster and, 187 Wikia and, 204–5 Wikipedia and, 161–62, 203–4 Alcoholics Anonymous (AA), 148 Alexa, 236–37 Alexander, Alastair, 208 Allman, Eric, 163–64 amateur creativity, 33, 90, 103 deregulation of, 254–59 in music, 24–29, 32–33 Amazon, 48–49, 125–26, 129–30, 141, 142, 239–40 Little Brother and, 132, 136 Amazon Web Services (AWS), 126, 138 Anderson, Chris, 129 Andover.net, 199 anime music videos (AMVs), 77–78, 79, 80 AOL, 153–54 Apache, 164–65, 183, 241–42 Apple, 88, 142, 165 iPod, 41, 46, 47, 88 iTunes, 12, 13, 41–42, 134 Armstrong, Edwin Howard, 30 Armstrong, Louis, 104 80706 i-xxiv 001-328 r4nk.indd 319 astronomy, 170–71 Atmo, 73 AudioMulch, 12 Awesometown, 227 Bainwol, Mitch, 114 Baker, Stewart, 89 Barish, Stephanie, 80 Barlow, John Perry, 67 barter economies, 180 Baumol, William, 230 Beam-it, 135 Beatles, 74, 75, 255 Becker, Don, 180 Behlendorf, Brian, 164, 183, 242–43 Benkler, Yochai, 50, 58, 59, 62, 118, 140, 146, 169, 172, 176, 178 Berners-Lee, Tim, 58, 221 Bezos, Jeff, 125, 129, 136 Biancolo, Jim, 126 BigChampagne Online Media Measurement, 110 BIND, 164 Blair, Tony, 73–74, 273 blip.tv, 249 Blockbuster, 123–24, 129, 142 blogs, 57, 58–62, 63–65, 103, 139, 291 value of, 92–93 books, 42, 99–100, 268, 269, 291, 292 access to, 43–44 8/12/08 1:56:29 AM 320 books (cont.)
The Mesh: Why the Future of Business Is Sharing by Lisa Gansky
Airbnb, Amazon Mechanical Turk, Amazon Web Services, banking crisis, barriers to entry, carbon footprint, cloud computing, credit crunch, crowdsourcing, diversification, Firefox, Google Earth, Internet of things, Kickstarter, late fees, Network effects, new economy, peer-to-peer lending, recommendation engine, RFID, Richard Florida, Richard Thaler, ride hailing / ride sharing, sharing economy, Silicon Valley, smart grid, social web, software as a service, TaskRabbit, the built environment, walkable city, yield management, young professional, Zipcar
These businesses are relatively easy to start and are spreading like wildfire: bike sharing, home exchanges, fashion swap parties, energy cooperatives, shared offices, cohousing, music studios, tool libraries, food and wine cooperatives, and many more. They leverage hundreds of billions of dollars in available information infrastructure—telecommunications, mobile technology, enhanced data collection, large and growing social networks, mobile SMS aggregators, and of course the Web itself. They efficiently employ horizontal business to business services, such as FedEx, UPS, Amazon Web Services, PayPal, and an ever-increasing number of cloud computing services. All the Mesh businesses rely on a basic premise: when information about goods is shared, the value of those goods increases, for the business, for individuals, and for the community. Mesh businesses are legally organized as for-profit corporations, cooperatives, and nonprofit organizations. Once I started looking, I quickly uncovered over 1,500 relevant companies and organizations.
Data-Rich, Highly Shareable Goods and Services Mesh Best. five flavors of the big Mesh. Not all large companies can adopt a Mesh strategy as thoroughly and successfully as Netflix. Many can, will, and have adopted aspects of the Mesh where they’re able to perceive the competitive advantages. Here are five ways: 1. Provide services or platforms that enable and encourage Mesh businesses. We’ve already discussed several, such as Amazon Web Services, PayPal, and FedEx. Like many other companies, the streaming music service Pandora extends its service through an iPhone app. The iPad, Kindle, and Kno are strong new platforms for distributing e-books, blogs, video, and magazines. These services are likely to be reshaped in even more favorable ways by the Mesh as it grows. A very important category of Mesh-enablers is the social networks, such as Twitter, Buzz, MySpace, LinkedIn, and Facebook. 2.
Puppet 3 Cookbook by John Arundel
If you had to build the servers by hand every time, this process would be too lengthy to make it worthwhile, but with automated configuration management, it's a snap. There are plenty of cloud service providers around, with Amazon's EC2 being one of the best-known and oldest-established. In this recipe I'll show you how to manage cloud instances using EC2, but the general principles and most of the code will be adaptable to any cloud provider. Getting ready… You'll need an Amazon Web Services (AWS) account if you don't already have one. Of course if you're using an EC2 instance to try out the recipes in this book, you'll already have everything you need, but otherwise you can sign up for an AWS account here: http://aws.amazon.com/ You'll need the AWS access key ID and secret access key corresponding to your account. You can find these on the Security Credentials page of the AWS portal.
How it works… puppet config print will output every configuration parameter and its current value (and there are lots of them). To see the value for a specific parameter, add it as an argument to the puppet config print command: ubuntu@cookbook:~/puppet$ puppet config print noop false See also The Generating reports recipe in this chapter 251 Index A B a2ensite command 148 Amazon EC2 tab 188 Amazon Web Services (AWS) 188 Apache servers about 144 managing 144, 145 Apache virtual hosts creating 145-147 custom domains 149 docroots 149 working 148, 149 APT package framework 106 arguments passing, to shell commands 84, 85 array iteration using, in templates 96, 97 arrays about 45 creating 45 creating, with split function 46 hashes, using 46 using 45 working 45 arrays, of resources using 58 audit metaparameter 140 Augeas tool about 87 automatically editing config files with 89, 90 auto_failback setting 171 automatic syntax checking Git hooks, used 29 AWS Management Console URL 188 Blueprint installing 214 running 215, 216 C case statements about 50, 52 default value, specifying 52 examples 50 working 51 CentOS 8 changes parameter 90 check_syntax.sh script 32 circular dependency about 245 fixing 248 class inheritance extra values, adding using +> operator 78 using 75, 76 cloud computing 188 command output logging 237, 238 working 238 community Puppet style about 34 indentation 34 parameters 35 quoting 34 symlinks 36 using 34 variables 35 conditional statements comparisons 48 elsif branches 48 example 47 expressions, combining 48 working 47 writing 47 config files building with snippets 91- 93 editing automatically with Augeas 89-91 adding lines to 88, 89 configuration data importing, with Hiera 206-208 configuration settings inspecting 251 contain_package assertion 220 cron Puppet, running from 18, 19 cron jobs distributing 126-128 cross-platform manifests writing 79 custom facts creating 200, 201 D Dean Wilson example, URL 231 debug messages logging 239 resource, checking 240 variable values, printing 240 decentralized Puppet architecture creating 14, 16 definitions creating 59 using 59 working 60 dependencies using 61- 64 dependency graphs drawing 245-247 directory trees distributing 135, 136 DocumentRoot parameter 150 dotfiles 121 DOT format, graphs expanded_relationships.dot 247 254 relationships.dot 247 resources.dot 247 dry run mode using 236, 237 dynamic information importing 83 E EC2 instances AWS example, creating 188-193 managing 188 ENC.
Webbots, Spiders, and Screen Scrapers by Michael Schrenk
An example of a SOAP call is shown in Listing 26-13. In typical SOAP calls, the SOAP interface and client are created and the parameters describing requested web services are passed in an array. With SOAP, using a web service is much like calling a local function. If you'd like to experiment with SOAP, consider creating a free account at Amazon Web Services. Amazon provides SOAP interfaces that allow you to access large volumes of data at both Amazon and Alexa, a web-monitoring service (http://www.alexa.com). Along with Amazon Web Services, you should also review the PHP-specific Amazon SOAP tutorial at Dev Shed, a PHP developers' site (http://www.devshed.com). PHP 5 has built-in support for SOAP. If you're using PHP 4, however, you will need to use the appropriate PHP Extension and Application Repository (PEAR, http://www.pear.php.net) libraries, included in most PHP distributions.
Mastering Pandas by Femi Anthony
Amazon Web Services, correlation coefficient, correlation does not imply causation, Debian, en.wikipedia.org, Internet of things, natural language processing, p-value, random walk, side project, statistical model
There has been a proliferation of digital data input devices such as cameras and wearables, and the cost of huge data storage has fallen rapidly. Amazon Web Services is a prime example of the trend toward much cheaper storage. The Internetification of devices, or rather Internet of Things, is the phenomenon wherein common household devices, such as our refrigerators and cars, will be connected to the Internet. This phenomenon will only accelerate the above trend. Velocity of big data From a purely technological point of view, velocity refers to the throughput of big data, or how fast the data is coming in and is being processed. This has ramifications on how fast the recipient of the data needs to process it to keep up. Real-time analytics is one attempt to handle this characteristic. Tools that can help enable this include Amazon Web Services Elastic Map Reduce. At a more macro level, the velocity of data can also be regarded as the increased speed at which data and information can now be transferred and processed faster and at greater distances than ever before.
That Used to Be Us by Thomas L. Friedman, Michael Mandelbaum
3D printing, Affordable Care Act / Obamacare, Albert Einstein, Amazon Web Services, American Society of Civil Engineers: Report Card, Andy Kessler, Ayatollah Khomeini, bank run, barriers to entry, Berlin Wall, blue-collar work, Bretton Woods, business process, call centre, carbon footprint, Carmen Reinhart, Cass Sunstein, centre right, Climatic Research Unit, cloud computing, collective bargaining, corporate social responsibility, Credit Default Swap, crowdsourcing, delayed gratification, energy security, Fall of the Berlin Wall, fear of failure, full employment, Google Earth, illegal immigration, immigration reform, income inequality, job automation, Kenneth Rogoff, knowledge economy, Lean Startup, low skilled workers, Mark Zuckerberg, market design, more computing power than Apollo, Network effects, obamacare, oil shock, pension reform, Report Card for America’s Infrastructure, rising living standards, Ronald Reagan, Rosa Parks, Saturday Night Live, shareholder value, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, the scientific method, Thomas L Friedman, too big to fail, University of East Anglia, WikiLeaks
The beauty of the cloud, and the reason that it is driving the flattening further and faster, is that it can turn any desktop, laptop, or simple handheld device with a browser into an information-creation or -consumption powerhouse by serving as a central location for those myriad applications, which run on individual user’s devices. Amazon.com, for example, is now selling not only books and chain saws but business facilities in the cloud. Andy Jassy is Amazon’s senior vice president in charge of Amazon Web Services, Bloomberg BusinessWeek explained (March 3, 2011), which means that his job is to rent space to individual innovators, or companies, on Amazon’s rent-a-cloud. Although all shoppers are welcome, this Amazon, [Jassy] explains, is for business customers and isn’t well marked on the home page. It’s called Amazon Web Services, or AWS … , which rents out computing power for pennies an hour. “This completely levels the playing field,” Jassy boasts. AWS makes it possible for anyone with an Internet connection and a credit card to access the same kind of world-class computing systems that Amazon uses to run its $34 billion-a-year retail operation … AWS is growing like crazy.
ACT Adams, Henry Adams, John Advanced Research Projects Agency Afghanistan; Soviet invasion of; U.S. war in African Americans; education of; in World War II Age of Fracture (Rodgers) Ahansal, Mustapha Ahmetovic, Belma Air Force, U.S. Alabama, University of; Creative Campus Alibaba Alice’s Adventures in Wonderland (Carroll) Alito, Samuel Allegheny College Allen, Woody All in the Family (television show) Alonzo, Amanda Alpoge, Levent al-Qaeda Aman, Peter Amanpour, Christiane Amazon; Web Services (AWS) America COMPETES Act (2007) American Association of Retired Persons (AARP) American Federation of State, County and Municipal Employees American Federation of Teachers American Interest, The Americans and the California Dream (Starr) American Society of Civil Engineers (ASCE) American Solutions American Telephone and Telegraph (AT&T) Amtrak Acela Anand, Namrata Andersen, Kurt Anderson, Chris Android Angelides, Phil anti-Federalists AOL Apollo space program Apotheker, Léo Apple; iPad; iPhone; iPod; Macintosh computers Applied Materials apps Arab oil embargo Arab world, uprisings in Argonne National Laboratory Arkansas Armey, Dick Army, U.S.; Training and Doctrine Command Asato, Cathy Asia Society, Center on U.S.
The Stack: On Software and Sovereignty by Benjamin H. Bratton
1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Jony Ive, Julian Assange, Khan Academy, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, performance metric, personalized medicine, Peter Thiel, phenotype, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, WikiLeaks, working poor, Y Combinator
It is at this level of The Stack that the modern coherence of the state, which would produce one sort of public, and the operations of platforms, which would produce another, can come into conflict, overlapping and interlacing one another without universal jurisdiction or resolution, but it is also where they can reinforce each other with more pervasive forms of ambient governance. The geopolitics of the Cloud is everywhere and wants everything: the platform wars between Google, Facebook, Apple, and Amazon, anonymized servers routing the angry tweets from street battles, Anonymous going up against Mexican drug cartels, WikiLeaks crowd-sourcing counterespionage, Tor users building on top of Amazon Web Services services, carriers licensing content, content providers licensing bandwidth, proprietary fiber networks connected trading centers, and on, and on. It might seem at first blush that these events, each perhaps pushing legal boundaries in its own way, should be understood as disruptive contaminations of a standing political order—acts of resistance to the system, even. Yet in their own consistency, this stockpile of exceptions is probably better interpreted as part of the constitution of another emergent order (a nomos of the Cloud even?).
That said, as the Cloud is planetary is scope, state control of its systems is guaranteed only to the extent that private providers continue to respect (by consent, force, joint venture, outright merger) the practical sovereignty of the national jurisdictions in which their servers are installed, where their offices are headquartered, how exactly their data structure economic exchanges, and how their monetization of those data is or is not taxed. That arrangement is both resilient and unstable in various measures and tracks the successes and failures of globalization itself. Today most Cloud service providers have constraining jurisdictions built into service plans. For example, Amazon Web Services segregates the serving of hosted data according to several geographic “availability zones,” allowing developers to deploy specific versions of their application to specific users in specific countries according to local laws and priorities, regardless of where Amazon might be hosting or mirroring their data. However, the sorting of state space and data space is not always so neat, and the unintended effects of innovative interconnections between Cloud publics can be calamitous.
The platform does not care about your name or who you really are deep down, but only in the likelihood that the next presentation of object X, Y, or Z will motivate your One-Clicking and the subsequent activation of supply chain cascades that ensue. For this, Amazon does not even necessarily have to provide the User-facing front end for Cloud services. Just as UPS and FedEx moved into high-margin logistics consulting businesses, Amazon Web Services is a major provider of large-scale Cloud hosting and e-commerce for third parties (including states and their intelligence agencies). In addition to finding customers for its suppliers, Amazon also rents the pick-axes for the Cloud rush. This superterranean platform-of-platforms (from servers to warehouse to inventory delivery) allows both small and large affiliate actors to engage multiple overlapping and even opposed publics on the same shared hosting infrastructures.
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, British Empire, business intelligence, business process, call centre, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, crowdsourcing, David Ricardo: comparative advantage, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, game design, global village, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, inventory management, James Watt: steam engine, Jeff Bezos, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, Mars Rover, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, pattern recognition, payday loans, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K
Today, people with connected smartphones or tablets anywhere in the world have access to many (if not most) of the same communication resources and information that we do while sitting in our offices at MIT. They can search the Web and browse Wikipedia. They can follow online courses, some of them taught by the best in the academic world. They can share their insights on blogs, Facebook, Twitter, and many other services, most of which are free. They can even conduct sophisticated data analyses using cloud resources such as Amazon Web Services and R, an open source application for statistics.13 In short, they can be full contributors in the work of innovation and knowledge creation, taking advantage of what Autodesk CEO Carl Bass calls “infinite computing.”14 Until quite recently rapid communication, information acquisition, and knowledge sharing, especially over long distances, were essentially limited to the planet’s elite. Now they’re much more democratic and egalitarian, and getting more so all the time.
id=USARGDPH INDEX Academically Adrift: Limited Learning on College Campuses (Arum and Roksa) Acemoglu, Daron Affinnova Aftercollege.com Agarwal, Anant Age of Spiritual Machines, The: When Computers Exceed Human Intelligence (Kurzweil) Agrarian Justice (Paine) agriculture: development of inelastic demand in Ahn, Luis von Aiden, Erez Lieberman Airbnb.com Alaska, income guarantee plan in algorithms Allegretto, Sylvia Allstate Amazon Amazon Web Services American Society of Civil Engineers (ASCE) Android animals, domestication of Apple Arthur, Brian artificial intelligence (AI) future of SLAM problem in uses of see also robots Arum, Richard ASCI Red ASIMO Asimov, Isaac Asur, Sitaram Athens, ancient ATMs Audi Australia, immigrant entrepreneurship in Autodesk automation: future of labor market effects of in manufacturing Autor, David Baker, Stephen Barnes & Noble Bartlett, Albert A.
Digital Bank: Strategies for Launching or Becoming a Digital Bank by Chris Skinner
algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, bank run, Basel III, bitcoin, business intelligence, business process, business process outsourcing, call centre, cashless society, clean water, cloud computing, corporate social responsibility, credit crunch, crowdsourcing, cryptocurrency, demand response, disintermediation, don't be evil, en.wikipedia.org, fault tolerance, fiat currency, financial innovation, Google Glasses, high net worth, informal economy, Infrastructure as a Service, Internet of things, Jeff Bezos, Kevin Kelly, Kickstarter, M-Pesa, margin call, mass affluent, mobile money, Mohammed Bouazizi, new economy, Northern Rock, Occupy movement, platform as a service, Ponzi scheme, prediction markets, pre–internet, quantitative easing, ransomware, reserve currency, RFID, Satoshi Nakamoto, Silicon Valley, smart cities, software as a service, Steve Jobs, strong AI, Stuxnet, trade route, unbanked and underbanked, underbanked, upwardly mobile, We are the 99%, web application, Y2K
By doing this, Amazon built its business on finding offers that you might buy, because people like you buy it, and they know this thanks to our unique dataprints. Soon, Amazon was more of a behemoth of data, moving into selling anything from white goods to televisions, and it is easy to sell online when you know how to leverage data relationships. Even then, for Amazon, even this was not enough. In recognising its information leadership, Amazon opened Amazon Web Services (AWS) to become the largest cloud computing firm out there. Amazon now adds server systems to AWS every day that would have been the equivalent of the complete server architecture required to run the total retail business two years ago. That’s information warfare: leveraging systems expertise to get more share of wallet, expansion of market, growth of proposition, and development of the offer into a range of services with no dependency on one.
The Innotribe Start-up Challenge was designed to introduce the most promising FinTech and Financial Services Start-Ups to SWIFT’s community of more than 9,700 banking organisations, securities institutions and corporate customers in 209 countries. THE CURRENCY CLOUD, GLOBAL An interview with Michael Laven, Chief Executive, the Currency Cloud Cloud computing has been around for a while, but still suffers from some extreme views with some considering cloud to just be Amazon Web Services whilst others see this as a way of leveraging new forms of business models. Michael (Mike) Laven, CEO of Currency Cloud is one of the latter visionaries, and is changing the game in cross-border payments by building a cloud-based offer for currency movements and offering this as a low-cost, transparent service to consumers and small businesses. What is the Currency Cloud? There is a huge volume of cross-border transactions that are well served for the large, multinational firms, but a major, untapped and underserved market for the cross-border payments needs of smaller firms.
Messy: The Power of Disorder to Transform Our Lives by Tim Harford
affirmative action, Air France Flight 447, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Atul Gawande, autonomous vehicles, banking crisis, Barry Marshall: ulcers, Basel III, Berlin Wall, British Empire, Broken windows theory, call centre, Cass Sunstein, Chris Urmson, cloud computing, collateralized debt obligation, crowdsourcing, deindustrialization, Donald Trump, Erdős number, experimental subject, Ferguson, Missouri, Filter Bubble, Frank Gehry, game design, global supply chain, Googley, Guggenheim Bilbao, high net worth, Inbox Zero, income inequality, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, Mark Zuckerberg, Menlo Park, Merlin Mann, microbiome, out of africa, Paul Erdős, Richard Thaler, Rosa Parks, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, Stewart Brand, telemarketer, the built environment, The Death and Life of Great American Cities, Turing test, urban decay
Instead, over the next few years, Amazon launched products as disparate as the Kindle (which immediately and repeatedly sold out, as Amazon struggled to manufacture it), Mechanical Turk (an unsettlingly named global clearinghouse for labor, which pioneered crowdsourcing but was criticized as being a sweatshop), the Fire Phone (widely reviewed as ugly, weird, and disappointing), Marketplace (where competitors to Amazon would use Amazon’s own product listings to advertise their own cheaper alternatives), and Amazon Web Services. AWS in particular was a bold stroke—a move into cloud computing in 2006, four years ahead of Microsoft’s Azure and six years ahead of Google Compute. As Bezos liked to say during the crunches of 1998 and 1999, “If you are planning more than twenty minutes ahead in this environment, you are wasting your time.”15 He was a man in a hurry. No wonder he created such an almighty mess. • • • Come the Second World War, the Italians were the Germans’ allies, but they seemed as prone as ever to losing battles.
By Christmas 2009, Amazon had a market share in e-books of around 90 percent.34 One can tell almost exactly the same story about Amazon’s initially baffling venture into cloud computing: a crazy rush, a series of early technical problems, a loss-making price. Then within a few years, Amazon, a mere bookseller, was the dominant player in cloud computing, and analysts were pronouncing that Amazon Web Services was a more valuable business than Amazon’s online retailing operation. The opportunity had been there to take, but the titans of the industry, IBM, Google, Apple, and Microsoft, had all hesitated at the prospect of a costly battle with the upstart.35 Again and again, we see Amazon moving quickly, losing money, struggling to cope with the demand it created, and in the end, dominating a market.
Hadoop: The Definitive Guide by Tom White
Amazon Web Services, bioinformatics, business intelligence, combinatorial explosion, database schema, Debian, domain-specific language, en.wikipedia.org, fault tolerance, full text search, Grace Hopper, information retrieval, Internet Archive, linked data, loose coupling, openstreetmap, recommendation engine, RFID, SETI@home, social graph, web application
Machine logs, RFID readers, sensor networks, vehicle GPS traces, retail transactions—all of these contribute to the growing mountain of data. The volume of data being made publicly available increases every year, too. Organizations no longer have to merely manage their own data: success in the future will be dictated to a large extent by their ability to extract value from other organizations’ data. Initiatives such as Public Data Sets on Amazon Web Services, Infochimps.org, and theinfo.org exist to foster the “information commons,” where data can be freely (or in the case of AWS, for a modest price) shared for anyone to download and analyze. Mashups between different information sources make for unexpected and hitherto unimaginable applications. Take, for example, the Astrometry.net project, which watches the Astrometry group on Flickr for new photos of the night sky.
Setup First install Whirr by downloading a recent release tarball, and unpacking it on the machine you want to launch the cluster from, as follows: % tar xzf whirr-x.y.z.tar.gz Whirr uses SSH to communicate with machines running in the cloud, so it’s a good idea to generate an SSH keypair for exclusive use with Whirr. Here we create an RSA keypair with an empty passphrase, stored in a file called id_rsa_whirr in the current user’s .ssh directory: % ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa_whirr Warning Do not confuse the Whirr SSH keypair with any certificates, private keys, or SSH keypairs associated with your Amazon Web Services account. Whirr is designed to work with many cloud providers, and it must have access to both the public and private SSH key of a passphrase-less keypair that’s read from the local filesystem. In practice, it’s simplest to generate a new keypair for Whirr, as we did here. We need to tell Whirr our cloud provider credentials. We can export them as environment variables as follows, although you can alternatively specify them on the command line, or in the configuration file for the service
These events are first filtered and processed, and then handed to various backend systems, including AsterData, Hypertable, and Katta. The volume of these events can be huge, too large to process with traditional systems. This data can also be very “dirty” thanks to “injection attacks” from rogue systems, browser bugs, or faulty widgets. For this reason, ShareThis chose to deploy Hadoop as the preprocessing and orchestration frontend to their backend systems. They also chose to use Amazon Web Services to host their servers, on the Elastic Computing Cloud (EC2), and provide long-term storage, on the Simple Storage Service (S3), with an eye toward leveraging Elastic MapReduce (EMR). In this overview, we will focus on the “log processing pipeline” (Figure 16-19). The log processing pipeline simply takes data stored in an S3 bucket, processes it (described shortly), and stores the results back into another bucket.
Beautiful security by Andy Oram, John Viega
Albert Einstein, Amazon Web Services, business intelligence, business process, call centre, cloud computing, corporate governance, credit crunch, crowdsourcing, defense in depth, en.wikipedia.org, fault tolerance, Firefox, loose coupling, market design, Monroe Doctrine, new economy, Nicholas Carr, Nick Leeson, Norbert Wiener, optical character recognition, packet switching, performance metric, pirate software, Search for Extraterrestrial Intelligence, security theater, SETI@home, Silicon Valley, Skype, software as a service, statistical model, Steven Levy, The Wisdom of Crowds, Upton Sinclair, web application, web of trust, x509 certificate, zero day, Zimmermann PGP
Backing up to a central server is all well and good, but what happens if we get robbed and someone steals the PCs and the server? Enter the new world of web services and cloud computing. To mitigate the risk of catastrophic system loss, I wrote a simple plug-in (see the later section “Platforms of the Long-Tail Variety: Why the Future Will Be Different for Us All” on page 165) to the home server that makes use of the Amazon Web Services platform. At set intervals, the system copies the directories I chose onto the server and connects via web services to Amazon’s S3 (Simple Storage System) cloud infrastructure. The server sends a backup copy of the data I choose to the cloud. I make use of the WS-Security specification (and a few others) for web services, ensuring the data is encrypted and not tampered with in transport, and I make use of an X.509 digital certificate to ensure I am communicating with Amazon.
He served on the Roundtable on Scientific Communication and National Security, a collaborative project of the National Research Council and the Center for Strategic and International Studies. 268 CONTRIBUTORS INDEX Numbers 3-D Secure protocol account holder domain, 76 acquirer domain, 76 e-commerce security and, 76–78 evaluation of, 77 issuer domain, 76 transaction process, 76 802.11b standard, 51, 52 802.11i standard, 51 A ABA (American Bar Association), 203 Access Control Server (ACS), 77 accountability, 213, 214 ACS (Access Control Server), 77 ActionScript, 93 ad banners (see banner ads) Adams, Douglas, 158 Advanced Monitor System (AMS), 254, 256 advertising (see online advertising) adware (see spyware) Aegenis Group, 66 Agriculture, Department of, 196 AHS (Authentication History Server), 77 AI (artificial intelligence), 254, 257 AllowScriptAccess tag, 94 Amazon Web Services platform, 152 Amazon.com, 102 American Bar Association (ABA), 203 AMS (Advanced Monitor System), 254, 256 analyst confirmation traps, 12 Anderson, Chris, 165 Andreessen, Marc, 165, 166 Anna Carroll (barge), 206 anti-executables, 253 anti-spyware software evolution of, 251 initial implementation, 251 intrusive performance, 254 strict scrutiny, 252 anti-virus software diminished effectiveness, 249 functional fixation, 15 functionality, 232 historical review, 248–249 honeyclients and, 141 intrusive performance, 254 malware signature recognition, 251 need for new strategies, 248 strict scrutiny, 252 zero-day exploits and, 252 Apgar score, 37 Apgar, Virginia, 37 Apple Computer, 8 artificial intelligence (AI), 254, 257 Ascom-Tech AG, 117 Ashenfelter, Orley, 164 Aspect Security, 188 Atkins, Derek, 119 ATMs, early security flaws, 36 attacks (see malicious attacks) attribute certificates, 111 Attrition.org, 55 authentication 3-D Secure protocol, 77 auto-update and, 15 CV2 security code, 76 e-commerce security, 83, 84 federated programs, 210 NTLM, 6 password security, 7 PGP Global Directory and, 127 portability of, 85 security pitfall in, 71 SET protocol, 78 WEP support, 52 Authentication History Server (AHS), 77 authoritative keys, 123 authorization We’d like to hear your suggestions for improving our indexes.
The New Digital Age: Transforming Nations, Businesses, and Our Lives by Eric Schmidt, Jared Cohen
3D printing, access to a mobile phone, additive manufacturing, airport security, Amazon Mechanical Turk, Amazon Web Services, anti-communist, augmented reality, Ayatollah Khomeini, barriers to entry, bitcoin, borderless world, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, crowdsourcing, data acquisition, Dean Kamen, Elon Musk, failed state, fear of failure, Filter Bubble, Google Earth, Google Glasses, hive mind, income inequality, information trail, invention of the printing press, job automation, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, means of production, mobile money, mutually assured destruction, Naomi Klein, offshore financial centre, peer-to-peer lending, personalized medicine, Peter Singer: altruism, Ray Kurzweil, RFID, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, social graph, speech recognition, Steve Jobs, Steven Pinker, Stewart Brand, Stuxnet, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day
Building platforms that we merely hope alienated youth will like and use is the equivalent of dropping propaganda flyers from an airplane. Outsiders don’t have to develop the content; they just need to create the space. Wire up the city, give people basic tools and they’ll do most of the work themselves. A number of technology companies have developed start-up kits for people to build applications on top of their platforms; Amazon Web Services and Google App Engine are two examples, and there will be many others. Creating space for others to build the businesses, games, platforms and organizations they envision is a brilliant corporate maneuver, because it ensures that a company’s products are used (boosting brand loyalty, too) while the users actually build and operate what they want. Somalis will build apps that are effective antiradicalization tools to reach other Somalis; Pakistanis will do the same for other Pakistanis.
INDEX Aadhaar Abbottabad, Pakistan, 2.1, 5.1 Abkhaz nationalists Abuja, Nigeria Academi, LLC accountability, 2.1, 4.1, 6.1, 7.1 activist groups additive manufacturing Advanced Research Projects Agency (ARPA), n Afghanistan, 1.1, 4.1, 5.1, 5.2, 5.3, 6.1, 6.2, 7.1 reconstruction of, 7.1, 7.2, 7.3 Africa, 3.1, 4.1, 4.2 African Americans African National Congress (ANC) African Sahel African Union Age of Spiritual Machines, The: When Computers Exceed Human Intelligence (Kurzweil), con.1 Agha-Soltan, Neda Agie, Mullah Akbar Agreement on Trade-Related Aspects of Intellectual Property Rights (1994) Ahmadinejad, Mahmoud al-Aqsa Martyrs Brigades al-Assad, Bashar Alcatel-Lucent AlertNet Algeria, 3.1, 4.1 alienation Al Jazeera al-Qaeda, 5.1, 5.2, 5.3, 5.4, 5.5, con.1 al-Shabaab, 2.1, 5.1, 7.1, 7.2 Amazon, itr.1, 1.1, 1.2 data safeguarded by Amazon Web Services American Sentinel drone Android anonymity, 2.1, 3.1, 4.1 Anonymous, 5.1, 5.2 Anti-Ballistic Missile Treaty antiradicalization antiterrorism units, 5.1, 5.2, 5.3, 5.4 Apple, itr.1, 5.1 data safeguarded by apps, 2.1, 5.1 Arab Spring, itr.1, 4.1, 4.2, 4.3, 4.4, 4.5 AR.Drone quadricopter Argentina Armenia arms-for-minerals trade arrests artificial intelligence (AI), itr.1, 1.1 artificial pacemakers Asia Asia-Pacific Economic Cooperation (APEC) Assange, Julian, 2.1, 2.2, 2.3, 2.4, 5.1 Astroturfing Atatürk, Mustafa Kemal, 3.1, 3.2 Athar, Sohaib, n, 269 ATMs augmented reality (AR), itr.1, 2.1 autocracies, 2.1, 3.1, 3.2 data revolution in dissent in information shared by online discussions in Ayalon, Danny Baghdad Baghdad Museum Bahrain Baidu.com, n Bamiyan Buddhas Bangladesh bank loans Basque separatists Batbold, Sukhbaatar battery life Bechtel Belarus Belgium Ben Ali, Zine el-Abidine, 4.1, 4.2 Berezovsky, Boris Better Angels of Our Nature, The (Pinker), 6.1 big data challenge Bill of Guarantees bin Laden, Osama, 2.1, 5.1, 5.2, 6.1, nts.1 biometric information, 2.1, 2.2, 6.1, 6.2, 6.3 Bitcoin, 2.1, nts.1 BlackBerry Messenger (BBM), 2.1, 2.2, 4.1, 5.1 Black Hat Blackwater Blockbuster, n Bloomberg News Bluetooth, 2.1, 2.2, 6.1 body scan body temperatures Boko Haram Bosnia brand Brand, Stewart, n Brazil, 5.1, 5.2, 5.3 Bush, George H.
Airbnb, Amazon Web Services, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing, en.wikipedia.org, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Mark Zuckerberg, minimum viable product, move fast and break things, Network effects, Oculus Rift, Paul Graham, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software as a service, software is eating the world, Steve Jobs, Steven Levy, Y Combinator
At Amazon, Bezos mandated that every internal product or feature should have an API. This means that it can be more easily shared (and used) by internal product and development teams. But it also means that the option exists to allow external developers to use it as well. The added bonus is that APIs lay the groundwork for the productisation of internal tools. That simple declaration created an IT, as well as cultural, architecture that catalysed the growth of Amazon Web Services. Within a few short years of its launch in 2006, the service was already a billion-dollar business.2 In short, small teams can run fast and innovate because of their size and the fact that they’re not reliant on the technology from other teams. Move Fast and Break Things Facebook created a culture of agility, promoting a philosophy to ‘move fast and break things’.3 Mark Zuckerberg explained the company’s ‘hacker way’ in a letter to investors:4 Hackers try to build the best services over the long term by quickly releasing and learning from smaller iterations rather than trying to get everything right all at once … We have the words ‘Done is better than perfect’ painted on our walls to remind ourselves to always keep shipping.
Index Note: page numbers in bold refer to illustrations, page numbers in italics refer to information contained in tables. 99designs.com 111 500 Startups accelerator 136, 160 Accel Partners 3, 158, 261, 304, 321, 336, 383 accelerators 136, 159–60, 160 accountants 164, 316 accounting software 164 acquisition (of users) costs 148–9, 184, 236–7, 275–9, 282 and Facebook 271, 272, 273–4 for five hundred-million-dollar apps 327, 341–3 for hundred-million-dollar apps 252, 259, 266, 267–74, 275–84, 295–307 and incentive-based networks 270–1 international 295–307 for million dollar apps 136–7, 139, 140–51, 148–9, 153 and mobile social media channels 271–3, 272 and mobile user-acquisition channels 269–70 strategy 222–31 for ten-million-dollar apps 211–12, 213, 222–31, 236–7, 248–9 and traditional channels 268–9 and ‘viral’ growth 225, 278, 279–84 zero-user-acquisition cost 278 acquisitions 414–25 buying sustained growth 417–18 by non-tech corporations 418–20 initial public offerings 420–2 Waze 415–16 activation (user) 136, 137, 139, 153–4, 211–12, 213 Acton, Brian 54, 394 addiction, smartphone 30–1 Adler, Micah 269 administrators 409 AdMob 414–15 advertising 43 business model 67, 89–90 costs 140 and Facebook 271, 272, 273–4 mobile 148–9, 268–70, 272–3, 272 mobile social media 272–3, 272 mobile user-acquisition channels 269–70 outdoor 264 shunning of 42, 54–6 video ads 273 aesthetics 131 after product–market fit (APMF) 180 agencies 195–7, 264, 343 ‘agile coaches’ see scrum masters agile software development 192–3, 299, 315, 357, 377 Ahonen, Tomi 45 ‘aiming high’ 40–1 Airbnb 160, 301 alarm features 48 Albion 111 alerts 293 Alexa.com 146 Alibaba 227 ‘ALT tags’ 147 Amazon 7, 29, 131, 164, 227, 276, 366, 374–5, 401, 406 Amazon Web Services 374 American Express 347 Amobee 149 analytics 134–5, 149, 199, 205, 210, 212, 217–21, 294 and cohort analysis 287–8 Flurry 135, 149, 220 function 217–18 Google Analytics 135, 219–20, 345 limitations 284 Localytics 135, 221 and marketing 263 mistakes involving 218–19 Mixpanel.com tool 135, 217–18, 220–1, 287, 290–1, 345 Andreessen, Marc 180, 418–19 Andreessen Horowitz 72, 80, 180, 321, 383, 385, 418–19 Android (mobile operating system) 6, 23–4, 38, 415 advertising 274 audience size 119 beta testing 202 building apps for 116–22 and international apps 296 in Japan 306 scaling development and engineering 357–8 time spent on 26 and WhatsApp 55 Angel Capital Association 162 angel investors 154, 155–6, 323 AngelList 99, 131, 155, 159, 233 Angry Birds (game) 6, 42, 47, 57–8, 87, 89, 97 and application programming interface 36 delivering delight 207 design 131 funding 321 game in game 348–9 international growth 297–9 platform 117, 118 product extension 356 virality 282 annual offsites 379 annual revenue per user (ARPU) 215, 219, 232, 236 anonymity 43, 56–7 anti-poaching clauses 247 antidilution rights 245 API see application programming interface app descriptions 143 app development billion-dollar app 8, 389–425 CEO advice 406–13 getting acquired 414–25 people 395–405 process 390–1 five-hundred-million-dollar app 325–87 funding 328, 383–7 hiring staff 334–6, 337–40 killer product expansion 350–63 process 326–8 scaling 326, 330–6, 331–2 scaling marketing 341–9 scaling people 364–72, 377–9 scaling process 373–82 scaling product development 357–63 hundred-million-dollar app 251–324 international growth 295–307 process 252–4 product-market fit 255–6 retention of users 286–94 revenue engines 257–66, 275–85 user acquisition 267–74 million-dollar app 81–171 app Version 0.1 123–35 coding 133–4 design 129–33 feedback 127, 134–5 funding 152–60, 161–71, 176, 235–49 identity of the business 106–14 lean companies 115–22 metrics 136–9, 139 process 82–4 startup process 85–105 testing 126–8 user acquisition 140–51 ten-million-dollar app 173–249 growth engine 222–31, 235–49 metrics 211–21 new and improved Version 1.0 198–210 process 174–6 product–market fit 180–97 revenue engine 232–4 venture capital 235–49 app stores 22, 27–8, 33–4 see also Apple App Store; Google Play app-store optimisation (ASO) 142, 225 AppAnnie 205 Apple 19, 20, 31–2, 393 application programming interface 35–6 designers 129 Facetime app 46 iWatch 38–9 profit per employee 402–3 revenue per employee 401 visual voicemail 50 Worldwide Developers Conference (WWDC) 313 see also iPad; iPhone Apple App Store 22, 27, 32–3, 75, 88, 89, 117, 226 finding apps in 140, 141, 142–5 international apps 297–9 making submissions to 152–3 and profit per employee 403 ratings plus comments 204–5 Apple Enterprise Distribution 201–2 application programming interface (API) 35–6, 185, 360, 374 ARPU see annual revenue per user articles of incorporation 169 ASO see app-store optimisation Atari 20 Atomico 3, 261, 321, 383 attribution 227–31 for referrals 230–1 average transaction value (ATV) 214–15, 219, 232, 236, 387 Avis 95 backlinking to yourself 146 ‘bad leavers’ 247 Balsamiq.com 128 Banana Republic 352 bank accounts 164 banking 156–7 Bardin, Noam 43 Barr, Tom 338 Barra, Hugo 120, 306 Baseline Ventures 72 Baudu 226 beauty 131 BeeJiveIM 33 before product–market fit (BPMF) 180 ‘below the fold’ 143 Beluga Linguistics 297 Benchmark 75 benefits 398–400 beta testing 201–4 Betfair 358 Bezos, Jeff 366, 374 Bible apps 45 billion 9–10 Billion-Dollar Club 5 billionaires 9 Bing 226 ‘black-swan’ events 54 BlackBerry 23 Blank, Steve 257 Blogger 41 blood sugar monitoring devices 38 board seats 242, 243–4 board-member election consent 169 Bolt Peters 363 Booking.com 320 Bootstrap 145 Botha, Roelof 76, 77, 80 Box 7, 90, 276, 396–7, 411 brains 10 brainstorming 108 branding 111–13, 143, 263–4 Braun 129 Bregman, Jay xiii, 14–16, 95, 124, 209, 303 bridge loans 323 Brin, Sergey 366 Bring Your Own Infrastructure (BYOI) 17–18 Brougher, Francoise 340 Brown, Donald 44 Brown, Reggie 104–5 Bubble Witch 421 Buffet, Warren 4 build-measure-learn cycle 116 Burbn.com 72–4, 80 business advisors/coaches 103 business analysts 343 business culture 395–8 business goal setting 310–11 business models 67, 83, 87, 88–91, 175, 253, 259, 327, 351–2, 391, 400, 423–4 business success, engines of 183–4, 423–4 Business Wire 150 CAC see Customer Acquisition Cost Cagan, Marty 314 calendars 49 calorie measurement sensors 38 Cambridge Computer Scientists 160 camera feature 48 Camera+ app 48 Candy Crush Saga 6, 47, 87, 89, 131, 278–81, 318, 349, 421–2 card-readers 41–2 cash flow 164 CEOs see Chief Executive Officers CFOs see Chief Financial Officers channels incentive-based networks 270–1 mobile social-media 271–3, 272 mobile user-acquisition 269–70 source attribution 227–31 testing 224–7 traditional 268–9 viral 280–2 charging phones 49–50 Chartboost 149 chauffer hire see Uber app check-ins, location-based 72, 74 Chief Executive Officers (CEOs) 309, 380 advice from 406–13 and the long haul 68 and product centricity 185–6 role 337 Chief Financial Officers (CFOs) 316 Chief Operations Officers (COOs) 309, 326, 337–40, 380 Chief Technology Officers (CTOs) 186–7, 195 Chillingo 298 China 24–5, 146, 226, 306–7 Cisco 402 Clash of Clans (game) 6, 28, 36, 47, 87, 89, 97, 118, 227, 348–9, 398 Clements, Dave 120 Climate Corporation 412, 419 clock features 47 cloud-based software 67, 90 Clover 419 coding 133–4 cofounders 85, 91–105, 188, 191 chemistry 92–3 complementary skills 93 finding 96–9 level of control 94 passion 93–4 red flags 102–3 successful matches 104–5 testing out 100–2 cohort analysis 237, 287–8 Color.com (social photo-sharing) app 113, 255 colour schemes 111 Commodore 20 communication open 412–13 team 194 with users 208–9 Companiesmadesimple.com 163–4 computers 20–1, 29 conferences 97–8, 202, 312–13 confidentiality provisions 244 connectedness 30 ConnectU 105 consumer audience apps 233–4 content, fresh 147 contracts 165–6 convertible loans 163 Cook, Daren 112 cookies 228–9 Coors 348 COOs see Chief Operations Officers Cost Per Acquisition (CPA) 148–9 Cost Per Download (CPD) 148 Costolo, Dick 77–8, 79–80 costs, and user acquisition 148–9, 184, 236–7, 275–9, 282 Crash Bandicoot 33 crawlers 146–7 Cray-1 supercomputer 20 CRM see customer-relationship management CrunchBase 238 CTOs see Chief Technology Officers Customer Acquisition Cost (CAC) 148–9, 184, 236–7, 275–9 customer lifecycle 212–14 customer segments 346–7 customer-centric approach 344 customer-relationship management (CRM) 290–4, 343 customer-support 208–9 Cutright, Alyssa 369 daily active users (DAUs) 142 D’Angelo, Adam 75–6 data 284–5, 345–7 data engineers 284 dating, online 14, 87–8, 101–2, 263 decision making 379–82, 407–8 defining apps 31–4 delegation 407 delight, delivery 205–7 design 82, 129–33, 206–7 responsive 144 designers 132, 189–91, 363, 376 developer meetups 97 developers see engineers/developers development see app development; software development development agencies 196 ‘development sprint’ 192 Devine, Rory 358–9 Digital Sky Technologies 385 directors of finance 316–17 Distimo 205 DLD 97 Doerr, John 164, 310 Doll, Evan 42–3, 105 domain names 109–10 international 146 protection 145–6 Domainnamesoup.com 109 Dorsey, Jack 41, 58, 72, 75–7, 79–80, 104, 112, 215–16, 305, 312, 412–13 ‘double-trigger’ vesting 247 DoubleClick 414 Dow Jones VentureSource 64 down rounds 322–3 downloads, driving 150–1 drag along rights 245 Dribbble.com 132 Dropbox 7, 90, 131, 276 CEO 407, 410–11 funding 160 scaling 336 staff 399 Dunbar, Robin 364–5 Dunbar number 365 e-commerce/marketplace 28–9, 67, 89, 213–14 Chinese 306 Flipboard and 351–2 and revenue engines 232, 233–4, 276 social media generated 271–2 and user retention 288, 289 eBay 7, 28–9, 131, 180, 276 economic models 275 economies of scale 331–2, 331–2 eCourier 15, 95 education 68–9 edX 69 Ek, Daniel 357 Ellis, Sean 182 emails 291–3 emotion effects of smartphones on 29–30, 30 inspiring 223–4 employees see staff employment contracts 246–7 engagement 236, 278, 283 engineering VPs 337, 358–9 engineers/developers 190–1, 194–5, 361–2, 362, 370, 375–7, 405 enterprise 90, 233–4 Entrepreneur First programme 160 entrepreneurs 3–5, 7–8, 65, 262, 393–4, 409, 424 Ericsson 21 Etsy 107, 109, 110, 358 Euclid Analytics 149 Evernote 7, 90, 131, 399 ExactTarget 291 excitement 30 executive assistants 367 Exitround 419 experience 67–8, 264, 397 Fab.com 352 Facebook 7, 10, 26, 32, 48, 76, 226, 394, 422 and acquisition of users 271, 272, 273–4 acquisitions 416–18, 417 agile culture 375 alerts 293 and application programming interface 36 board 180 and business identity 114 and Candy Crush 280–1 Chief Executive Officer 406 cofounders 100–1 and Color 255–6 design 131, 206, 363 Developer Garage 97 driving downloads on 151 and e-commerce decisions 271, 272 and FreeMyApps.com 271 funding 419 and getting your app found 147 and the ‘hacker way’ 375 initial public offering 420–1 and Instagram 29, 51, 76–80, 90, 117 name 110 ‘No-Meeting Wednesday’ 376 product development 187 profit per employee 403 revenue per employee 401 scaling 336 and Snapchat 57 staff 339, 362, 363, 398, 401, 403 and virality 281 WhatsApp purchase 42, 54–6, 416–17, 417 zero-user-acquisition cost 278 and Zynga 279, 281 Facetime app 46 fanatical users 294 feedback 86, 127, 134–5, 182, 192–3, 198–201, 256, 396 loops 204, 211 qualitative 199 quantitative 199 see also analytics Feld, Brad 170, 241 Fenwick and West 168 Fiksu 264, 269–70 finance, VP of 317–18 finding apps 140–8, 148–9 FireEye 90 First Data 419 first impressions 107–10 Fitbit 38 fitness bracelets 38 flat rounds 322–3 Flipboard 6, 29, 42–3, 49, 51, 89–90 and application programming interface 36 Catalogs 351–2 cofounders 105 design 131, 207 funding 164 growth 351–2 platform choice 119 product innovation 351–2 user notifications 292 virality 281 zero-user-acquisition cost 278 Flurry 135, 149, 220 Fontana, Ash 233 Forbes magazine 40 Ford Motors 419 Founder Institute, The 168 founder vesting 166–7, 244 Foursquare 419 France Telecom 13 franchising 354 FreeMyApps.com 270–1 Friedberg, David 412 Froyo (Android mobile software) 7 Fujii, Kiyotaka 304 full service agencies 195–6 functionality 25–6, 45–50, 131 funding 72, 75–6, 84, 87–8, 152–60, 161–71, 179 accelerators 159–60 angel investors 154, 155–6, 323 for billion-dollar apps 391 convertible loans 163 core documents 169–70 for five-hundred-million-dollar apps 328, 383–7 founder vesting 166–7 for hundred-million-dollar apps 254, 258, 316–17, 318–24 incubators 159–60 legal aspects 163–4 and revenue engines 233–4 Series A 234, 238–40, 238, 240, 241, 242–6, 255, 319–21, 385 Series B 238, 241, 253, 260, 284, 319–21, 322, 384 Series C 384 signing a deal 167–8 for ten-million-dollar apps 152–60, 161–71, 176, 235–49 venture capital 72, 75, 156–8, 165–6, 235–49, 261–2, 383–5, 385, 418–19 game in game 348–9 gaming 42, 47, 318, 355 business model 67, 89 and revenue engines 232, 278–9 and user retention 288, 289 see also specific games Gandhi, Sameer 336 Gartner 271 Gates, Bill 4 general managers (GMs) 300–3 Gladwell, Malcolm 424 Glassdoor 361–2 Global Positioning System (GPS) 23 Gmail 72 GMs see general managers goal setting 40–1, 310–11 Goldberg, Dave 397 Goldman Sachs 385 ‘good leavers’ 247 Google 7, 19, 23, 27, 72, 88, 164, 226 acquisitions 43, 414–16, 418 application programming interface 35–6 beta testing 202 Chief Executive Officer 406–8 developer meetups 97 finding your app on 144, 147 Hangouts app 46 meetings 381–2 mission 404, 408–9 and the OKR framework 310 profit per employee 403, 405 revenue per employee 401, 405 scaling 332 and Snapchat 57 and source attribution 228–9 staff 339, 340, 361–2, 366, 401, 403, 404–5, 412 Thank God It’s Friday (TGIF) meetings 311–12 transparency 413 value 78 Waze app purchase 43 and WhatsApp 56 zero-user-acquisition cost 278 see also Android (mobile operating system) Google Ad Mob 149 Google AdSense 149 Google Analytics 135, 219–20, 345 Google Glass 38–9, 405 Google I/O conference 313 Google Maps 33, 35, 414, 416 Google Now 37 Google Play 88, 89, 117, 120, 226 and beta testing 202 finding apps in 141–5 profit per employee 403 ratings plus comments 204–5 Google Reader 72 Google Ventures 384 Google X 405 Google+ and business identity 114 and virality 281 Google.org 339 GPS see Global Positioning System Graham, Paul 184–5, 211 Graphical User Interface (GUI) 20 Greylock 321, 383 Gross, Bill 406–7, 409–10 Groupon 7, 51–2, 227, 344–5, 419 Grove, Andy 310 growth 267, 308–17 buying sustained 417–18 engines 184, 210, 222–31, 259, 265 and five-hundred-million-dollar apps 329–36 and Friday update meetings 311–12 and goal setting 310–11 and hiring staff 308–9, 411–12 and product and development teams 313–14 and staff conferences 312–13 targets 234, 260 see also acquisition (of users); international growth; scaling Growth Hackers 182 GUI see Graphical User Interface hackathons 99 Haig, Patrick 143 Hailo app xiii–xiv, 5, 36, 89, 386 big data 284–5 branding 112–13 cofounders 94–6 customer segments 346–7 customer-support 208–9 design 131, 132, 133, 206–7 development 123–7, 153–4 Friday update meetings 311 funding 162, 242 goal setting 310 growth 296–7, 299, 302–4, 308–11, 313, 315–17, 329–30, 334–6 hiring staff 308–9, 334–6, 338, 366–7 idea for 14–18 international growth 296, 297, 299, 302–4 market research 182 marketing 263, 264, 268, 270, 273, 341, 347–8 meetings 381 metrics 137–9, 216 name 107 organisational culture 396 platform choice 117, 120, 121 premises xiii–xiv, 177–8, 329–30, 371–2, 386 product development 189, 191, 196 retention 293–4 revenue engine 276 scaling development and engineering 357 scaling people 365–7 scaling process 377 team 258 testing 177–8, 201–4 and user emotionality 224 virality 280, 282 Hangouts app 46 Harris Interactive 31 HasOffers 149 Hay Day 47, 97 head of data 342 Heads Up Display (HUD) 38 heart rate measurement devices 37–8 Hed, Niklas 42 hiring staff 308–9, 334–6, 337–40, 365–70 history of apps 31–2 HMS President xiii–xiv, 177–9, 329, 371, 386 HockeyApp 202 HootSuite 151 Houston, Drew 407, 410–11 HP 180, 402 HTC smartphone 121 HUD see Heads Up Display human universals 44–5 Humedica 419 hyperlinks 147 hypertext markup language (HTML) 147 I/O conference 2013 202 IAd mobile advertising platform 149 IBM 20, 402 icons 143 ideas see ‘thinking big’ identity of the business 86 branding 111–13 identity crises 106–14 names 106–11 websites 113–14 image descriptions 147 in Mobi 149 in-app purchases 28 incentive-based networks 270–1 incorporation 163–4, 179 incubators 159–60 Index Ventures 3, 261 initial public offerings (IPOs) 64, 67–9, 78, 80, 246, 420–2 innovation 404–5 Instagram 6, 29, 48, 51, 67, 71–80, 88–90, 114, 117, 226, 278, 340, 417–18 cofounders 73–4 design 131 funding 75–6, 77–8 X-Pro II 75 zero-user-acquisition cost 278 instant messaging 46 Instantdomainsearch.com 109 integrators 410 Intel 310 intellectual property 165–6, 244, 247 international growth 295–307 Angry Birds 297–9 Hailo 296, 297, 299, 302–4 language tools 297 Square 295, 299, 304–6 strings files 296 Uber 299–302 International Space Station 13 Internet bubble 13 investment see funding iOS software (Apple operating system) 7, 23–4, 46, 75, 104 advertising 274 audience size 119 building apps for 116–22 and international apps 296 scaling development and engineering 357–8 time spent on 26 iPad 42–3, 118–20, 351 iPhone 6, 19, 22–3, 32, 38–9, 183, 351 advertising on 274 camera 48 designing apps for 117–18, 120 finding apps with 145 games 42, 47, 58 and Instagram 74–6 in Japan 306 and Square 104, 306 and Uber 301 user spend 117 and WhatsApp app 54–5 iPod 22 IPOs see initial public offerings Isaacson, Walter 32 iTunes app 22, 47, 88, 143 iTunes U app 69 Ive, Jony 129 iZettle 304 Jackson, Eric 40 Jain, Ankit 142 Japan 227, 304–6 Jawbone Up 38 Jelly Bean (Android mobile software) 7 Jobs, Steve 4, 22, 32, 323, 393, 425 journalists 150–1 Jun, Lei 306 Kalanick, Travis 299–300, 384, 422 Kayak 336 Keret, Samuel 43 Keyhole Inc. 414 keywords 143, 146 Kidd, Greg 104 King.com 349, 421–2 see also Candy Crush Saga KISSmetrics 291 KitKat (Android mobile software) 7 Klein Perkins Caulfield Byers (KPCB) 158, 261, 321, 383 Kontagent 135 Koolen, Kees 320, 339 Korea 30 Koum, Jan 42, 54, 55–6, 154, 321, 394, 416 Kreiger, Mike 73–6 language tools 297 Launchrock.com 113–14, 145, 202 Lawee, David 415 lawyers 103, 169, 170, 242 leadership 410–11 see also Chief Executive Officers; managers lean companies 69, 115–22, 154, 257, 320–1 Lee, Bob 340 legalities 163–70, 242–7, 301 letting go 406–7 Levie, Aaron 396–7, 411 Levinson, Art 32 LeWeb 97 Libin, Phil 399 licensing 356 life experience 67–8, 264 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291, 342 Line app 46, 226 Lingo24 297 LinkedIn 97, 226, 406, 408–9 links 147 liquidation preference 242, 243, 245 non-participating 245 Livio 419 loans, convertible 163 Localytics 135, 221 locations 69 logos 111–14 LTV see lifetime value luck 412 Luckey, Palmer 39 LVMH 304 Lyons, Carl 263 Maiden 95 makers 375–7 see also designers; engineers/developers managers 189–90, 300–3, 375–7, 405 MapMyFitness 419 market research 115, 127, 182 marketing data 345–7 and Facebook 271, 272, 273–4 and incentive-based networks 270–1 marketing engineering team 344–5 and mobile social media channels 271–3, 272 and mobile user-acquisition channels 269–70 partner marketing 347–8 scaling 341–9 teams 262–6, 337, 342 and traditional channels 268–9 VPs 262–6, 337, 342 marketplace see e-commerce/marketplace MasterCard 347–8 Matrix Partners 283 McClure, Dave 136, 160, 211, 234 McCue, Mike 42–3, 105, 351 McKelvey, Jim 41, 104 ‘me-too’ products 181 Medium 41 Meebo 73 meetings 379–82, 412–13 annual offsite 379 daily check-ins 381 disruptive nature 376–7 Friday update 311–12 meaningful 381–2 monthly strategic 380 quarterly 380 weekly tactical 380 Meetup.com 98–9 Mendelsen, Jason 170 messaging platforms 226 time spent on 46 and user retention 288, 289 metrics 136–9, 139, 211–21 activation 136, 137, 139, 153–4, 211–12, 213 annual revenue per user (ARPU) 215, 219, 232, 236 average transaction value (ATV) 214–15, 219, 232, 236, 387 consensual 215–16 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291, 342 and product-market fit 209–10 referral 137, 138, 139, 153, 154, 211–12, 213, 230–1 revenue 137, 138, 139, 154, 211–12, 213, 214–15, 219, 291 transparency regarding 312 see also acquisition (of users); retention (of users) mice 20 Microsoft application programming interface 35–6 revenue per employee 401 Windows 20, 22, 24 Millennial Media 149 minimum viable product (MVP) 123, 153 MirCorp 13–14 mission 261, 404, 408–9 Mitchell, Jason 51 Mitsui Sumitomo Bank 305 Mixpanel.com tool 135, 217–18, 220–1, 287, 290–1, 345 MMS see Multimedia Messaging Service Mobile Almanac 45 Mobile App Tracking 230, 231 mobile technology, rise of 19–39 MoMo app 306 Monsanto 419 moonshots 404–5 Moore, Jonathan 200 MoPub 149 Moqups.com 128 Mosaic 180 Motorola 21 Moz.com 143 Mullins, Jacob 419 Multimedia Messaging Service (MMS) 47 Murphy, Bobby 43, 104–5, 152–3 music player apps 47 MVP see Metrics into Action; minimum viable product names 106–11, 142 NameStation.com 108 Nanigans 273–4 National Venture Capital Association 64 native apps 33–4 NDA see Non Disclosure Agreement negotiation 265 Net Promoter Score (NPS) 206, 209 net-adding users 206 Netflix 400 Netscape 164, 180 New Enterprise Associates 385 New York Times news app 32–3, 256 news and alerts feature 48–9 Nextstop 72 Nguyen, Bill 255–6 NHN 227 Nike Fuelband 38 Nintendo Game Boy 47 Nokia 21, 35–6 Non Disclosure Agreement (NDA) 165 noncompetition/non-solicitation provision 244, 247 notifications 291–4 NPS see Net Promoter Score Oculus VR 39 OKR (‘objectives and key results’) framework 310–11, 380 OmniGraffle 128 open-source software 23, 34–5, 185 OpenCourseWare 68–9 operating systems 20–4 see also Android; iOS software operations VPs 337 org charts 258, 309 organisational culture 395–8 O’Tierney, Tristan 104 outsourcing 194–7 ownership and founder vesting 166–7 and funding 155, 156, 161–3, 318 oxygen saturation measurement devices 37–8 Paananen, Ilkka 118–19, 397–8 Page, Larry 4, 23, 382, 404, 407–8 Palantir 90 Palihapitiya, Chamath 187 Pandora 7, 47, 67, 131, 410 pay-before-you-download model 28 pay-per-download (PPD) 225 Payleven 304 payment systems 7, 33–4, 227, 304, 305 see also Square app PayPal 7, 227, 304, 305 Pepsi 196 Perka 419 perks 398–400 perseverance 67, 394, 410 personal computers (PCs) 29 perspiration measurement devices 38 Pet Rescue Saga 349, 421 Petrov, Alex 369 phablets 7 Pham, Peter 255 PhoneSaber 33 Photoshop 128 PIN technology 305 Pincus, Mark 311 Pinterest app 48, 226 and business identity 114 and e-commerce decisions 271, 272 and getting your app found 147 name 107 and virality 281 Pishevar, Shervin 300 pivoting 73–4 population, global 9–10 portfolio companies 261–2 PowerPoint 128 PPD see pay-per-download preferential return 243 premises 370–2 preparation 412 press kits 148, 150 press releases 150 Preuss, Dom 98 privacy issues 43, 56–7 private vehicle hire see Uber pro-rata rights 242, 243 producers 409 product chunks 360 product development scaling 357–63 scope 199 team building for 188–91 and team location 193–4 and vision 186–8, 191 see also app development; testing product expansion 350–63 product extension 354 product managers 189–90, 405 product-centricity 185–6, 314, 360 product-market fit 9, 180–97, 235–6, 248, 256–7 measurement 209–10, 212, 286–8 profit 267, 320, 342 profit margin 258–9, 318, 321 profit per employee 402–4, 403, 405 profitability 260, 277, 400 Project Loon 405 proms 12 proto.io tool 133 prototype apps 86, 174 app Version 0.1 123–35, 174 new and improved Version 1.0 198–210 rapid-design prototyping 132–3 PRWeb 150 PSP 47 psychological effects of smartphones 29–30, 30 pttrns.com 131 public-relations agencies 343 publicity 150–1, 225, 313 putting metrics into action 138–9 Puzzles and Dragons 47, 131 QlikView 221, 284–5 QQ 307 quality assurance (QA) 190–1, 196 Quora 76 QZone 307 Rabois, Keith 368, 369 Rakuten 227 Rams, Dieter 129 rapid-design prototyping 132–3 ratings plus comments 204–5 Red Bull 223 redemption codes 230 referrals (user) 137, 138, 139, 153, 154, 211–12, 213 attribution for referrals 230–1 referral codes 230 religious apps 45 remuneration 361–2, 362, 363 Renault 13 restated certification 169 retention (of users) 136–9, 153, 154 for five hundred-million-dollar apps 327, 341–3 for hundred-million-dollar apps 286–94, 288–9 measurement 286–8 for ten-million-dollar apps 206, 211–12, 213, 278 revenue 137–8, 139, 154, 211–12, 213, 214–15, 219, 236, 239–40, 267, 291, 331–2, 341–2, 354 revenue engines 184, 210, 232–4, 257–66, 265, 275–85 revenue per employee 400–2, 402, 405 revenue streams 27–9 Ries, Eric, The Lean Startup 115–16 Rockefeller, John D. 9 Rocket Internet 304 Rolando 33 Rosenberg, Jonathan 413 Rovio 58, 97, 118, 297–9, 318, 320–1, 336, 354, 409 see also Angry Birds Rowghani, Ali 77 Rubin, Andy 23 Runa 419 SaaS see software as a service Sacca, Chris 75–6 sacrifice 86–7 Safari Web browser 32 salaries 361–2, 362, 363 sales VPs 337 Salesforce 291 Samsung 23 Galaxy Gear smartwatch 38 smartphones 121 Sandberg, Sheryl 4, 100–1, 339, 397 SAP 304 scaling 259, 308, 312, 323–4, 326, 330–6, 331–2, 384–5 decision making 379–81 international growth 295–307 marketing 341–9 and organisational culture 396–8 people 338–9, 364–72 premature 334–5 process 373–82 product development and engineering 357–63 and product innovation 350–6 reasons for 333–4 skill set for 335–6 Schmidt, Eric 120 scope 199 screenshots 131, 144, 206 scrum masters (‘agile coaches’) 315, 359, 360 search functions 49 organic 141–2, 141, 145 search-engine optimisation (SEO) 142, 145–8, 225 Sedo.com 109 Seed Fund 136 Seedcamp 160 Sega Game Gear 47 segmentation 220, 287, 290, 346–7 self-empowered squads/units 360 SEO see search-engine optimisation Sequoia Capital 76, 77–80, 158, 255, 321, 383, 385 Series A funding 234, 238–40, 238, 240, 241, 242–6, 255, 261, 262, 319–21, 385 Series B funding 238, 241, 253, 260, 319–21, 322, 384 Series C funding 384 Series Seed documents 168 Sesar, Steven 263 sex, smartphone use during 31 Shabtai, Ehud 43 shares 156, 166–8, 244 ‘sharing big’ 51–2, 52 Shinar, Amir 43 Shopzilla 263 Short Message Service (SMS) 21, 46–7 Silicon Valley 71–4, 77, 79, 99, 162, 168, 180, 184, 255, 340, 361, 411, 422 Sina 227 sitemaps 146–7 skills sets complementary 93 diverse 409–10 for scaling 335–6 Skok, David 283 Skype app 7, 46, 111, 200–1, 226, 357, 419 Sleep Cycle app 48 Smartling 297 smartwatches 7, 38–9 SMS see Short Message Service Snapchat app 6, 43, 46, 56–7, 88, 89, 223, 226, 416, 418 cofounders 104–5 design 131 funding 152–3, 307, 320 name 107 platform 117 staff 340 valuations 333 virality 280, 283 zero-user-acquisition cost 278 social magazines 42–3 see also Flipboard social media 48 driving downloads through 151 and getting your app found 147 mobile channels 271–3, 272 and user retention 288, 289 Sofa 363 SoftBank 227 software development agile 192–3, 299, 315, 357, 377 outsourcing 194–5 see also app development software as a service (SaaS) 67, 90, 208, 214, 233, 276–7 Somerset House 329–30, 371 Sony 21, 47 SoundCloud 358 source attribution 227–31 space tourism 13–14 speech-to-text technology 50 speed 20 Spiegel, Evan 43, 56–7, 104–5, 152–3 Spinvox 50 Splunk 90 Spotify app 47, 357–8 SQL 284 Square app 6, 41–2, 58–9, 87, 89, 333, 350 branding 112 Chief Executive Officer 412–13 cofounders 104 design 131, 363 funding 320–1 international growth 295, 299, 304–6 marketing 348 metrics 215–16 name 107, 110 product–market fit 183 revenue engine 276 scaling people 367–8 scaling product innovation 352–3 staff 340, 367–8 transparency 312 virality 282 Square Cash 353 Square Market 353 Square Register 350, 352–3 Square Wallet 348, 350, 353 Squareup.com 144 staff at billion-dollar app scale 395–405, 423 attracting the best 91 benefits 398–400 conferences 312–13 conflict 334, 378 employee agreements 244 employee legals 246–7 employee option pool 244 employee-feedback systems 378 firing 370, 378 hiring 308–9, 334–6, 411–12 induction programmes 370 investment in 360 mistakes 369–70, 411–12 and premises 370–2 profit per employee 402–4, 403 revenue per employee 400–2, 402 reviews 370 scaling people 364–72, 377–9 scrum masters 315, 359, 360 training programmes 370 see also cofounders; specific job roles; teams Staples 419 Starbucks 338, 348 startup weekends 98 startups, technology difficulties of building 63–80 failure 63–5, 73–4 identity 106–14 lean 115–22, 154 process 82–4, 85–105 secrets of success 66–9 step sensors 38 stock markets 420–1 straplines 111 strings files 296 Stripe 160 style 111 subscriptions 90 success, engines of 183–4, 423–4 SumUp 304 Supercell 28, 47, 97, 118–19, 318, 336, 397–8, 401, 403 see also Clash of Clans; Hay Day SurveyMonkey 397 surveys 206, 209 synapses 10 Systrom, Kevin 71–80 tablets 7 Tableau Software 90 Taleb, Nicholas Nassim 54 Tamir, Diana 51 Tap Tap Revolution (game) 42 Target 419 taxation 164 taxi hailing apps see Hailo app TaxiLight 16 team builders 264 team building 188–91 teams 82, 174, 252, 390 complementary people 409–10 for five-hundred-million-dollar apps 326, 342–5, 357–63, 374, 386 growth 313–14, 326, 342–4 for hundred-million-dollar apps 258–61 located in one place 193–4 marketing 262–6, 342–4 marketing engineering 344–5 product development and engineering 357–63 ‘two-pizza’ 374 TechCrunch Disrupt 97, 99 technology conferences 97–8, 202, 312–13 Techstars 159, 160, 168 Tencent 307 Tencent QQ 226 term sheets 168, 169, 170, 243–4 testing 126–8, 177–8, 187–8, 192–3, 199–201 beta 201–4 channels 224–7 text messaging 21 unlimited packages 42 see also Short Message Service ‘thinking big’ 40–59, 82, 85 big problem solutions 41–3 disruptive ideas 53–9 human universals 44–5 sharing big 51–2, 52 smartphones uses 45–50 Thoughtworks 196 time, spent checking smartphones 25–6, 26, 45–50 Tito, Dennis 13 tone of voice 111 top-down approaches 311 traction 233, 252 traffic information apps 43 traffic trackers 146 translation 296–7 transparency 311–12, 412–13 Trilogy 13 Tumblr 110, 226, 399, 418 Twitter 41, 48, 54, 72, 226, 394 acquisitions 418 and application programming interface 36 and Bootstrap 145 and business identity 114 delivering delight 206 and e-commerce decisions 272 and FreeMyApps.com 271 funding 419, 421 and getting your app found 147 initial public offering 421 and Instagram 51, 76–7, 79–80 name 110 and virality 281 ‘two-pizza’ teams 374 Uber 6, 36, 87, 89, 333, 350 and attribution for referrals 231 design 131 funding 320, 384, 422 international growth 295, 299–302 name 107, 110 revenue engine 276 revenue per employee 401 scaling product innovation 355–6 staff 339, 399 user notifications 292 virality 280 Under Armour 419 Union Square Ventures (USV) 3, 158, 242, 261, 262, 288, 321, 323, 377, 383 unique propositions 198 UnitedHealth Group 419 URLs 110 ‘user experience’ (UX) experts 190 user journeys 127–8, 213–14 user notifications 291–4 user stories 193 users 83, 175, 252, 327, 390 activation 136, 137, 139, 153–4, 211–12, 213 annual revenue per user (ARPU) 215, 219, 232, 236 communication with 208–9 definition 137 emotional response of 223–4 fanatical 294 finding apps 140–8 lifetime value (LTV) 184, 215, 219, 220–1, 232, 275–7, 279, 291 metrics 136–9 net-adding of 206 ratings plus comments 204–5 referrals 137, 138, 139, 153, 154, 211–12, 213, 230–1 target 83, 115, 127 wants 180–97 see also acquisition (of users); retention (of users) Usertesting.com 200–1 USV see Union Square Ventures valuations 83, 161–3, 175, 237–8, 238, 253, 318, 319, 322, 327, 333, 391 venture capital 72, 75, 156–8, 165–6, 235–49, 261–2, 383–5, 385, 418–19 Viber app 6, 46, 1341 video calls 46, 47 viral coefficient 282–4 ‘viral’ growth 225, 278, 279–84 Communication virality 281 and cycle time 283–4 incentivised virality 280–1 inherent virality 280 measurement 282–4 social-network virality 281 word-of-mouth virality 281–2 virtual reality 39 vision 261, 393–4, 408–9, 414, 415 voice calls 46–7 voice-over-Internet protocol (VOIP) 46 voicemail 50 Wall Street Journal 43, 55 warranties 246 Waze app 6, 43, 97 acquisition 415–16 design 131 name 107 zero-user-acquisition cost 278 web browsing 49 Web Summit 97 websites 113–14, 144–8 WebTranslateIt (WTI) 297 WeChat app 46, 226, 306 Weibo 48 Weiner, Jeff 408–9 Wellington Partners 4 Weskamp, Marcos 207 Westergren, Tim 410 WhatsApp 6, 42, 46, 54–6, 87, 90, 226, 394 acquisition 42, 54–6, 416, 416–17, 417 cofounders 96 design 131, 144 funding 154, 320–1 platform 117–18 valuations 333 virality 280 White, Emily 340 Williams, Evan 41, 65 Williams, Rich 344 Wilson, Fred 110, 242, 288, 323, 377 Windows (Microsoft) 20–1, 22, 24, 24 Winklevoss twins 105 wireframes 127–8 Woolley, Caspar 15–16, 95, 124, 338 WooMe.com 14, 87–8, 101–2, 263 Workday 90 world population 9–10 Worldwide Developers Conference (WWDC) 313 wowing people 8–9 WTI see WebTranslateIt Xiaomi 306 Y Combinator 159–60, 184–5, 211, 407, 410–11 Yahoo!
3D printing, additive manufacturing, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, asset-backed security, augmented reality, barriers to entry, bitcoin, bounce rate, business intelligence, business process, business process outsourcing, call centre, capital controls, citizen journalism, Clayton Christensen, cloud computing, credit crunch, crowdsourcing, disintermediation, en.wikipedia.org, George Gilder, Google Glasses, high net worth, I think there is a world market for maybe five computers, Infrastructure as a Service, invention of the printing press, Jeff Bezos, jimmy wales, London Interbank Offered Rate, M-Pesa, Mark Zuckerberg, mass affluent, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, optical character recognition, performance metric, platform as a service, QWERTY keyboard, Ray Kurzweil, recommendation engine, RFID, risk tolerance, self-driving car, Skype, speech recognition, stem cell, telepresence, Tim Cook: Apple, transaction costs, underbanked, web application
—Carmen Herranz, director of innovation at BBVA6 However, much earlier than the BBVA move, some of the more progressive institutions were already floating the concept of moving core banking systems into the cloud for the same reasons—improved productivity, decision making, portability and speed. In May 2010, Michael Harte, CIO of Commonwealth Bank in Australia, announced CBA’s intention to set up a cloud-based operation with Amazon Web Services. Harte explained the rationale behind this move as looking to reduce the cost of purchasing IT and related infrastructure by paying for services on demand as CBA grew, especially as reliance on more digital integration and real-time engagement became essential to CBA’s customer experience. In December 2011, Deutsche Bank went live with its first phase of cloud deployment, namely its IaaS (Infrastructure as a Service) development platform.
Deutsche Bank, mentioned earlier, has also developed new modular data centre designs and elastic computing platforms, underpinned by its core identity management platforms, including a Microsoft Active Directory system and an SAP-linked Global LDAP directory. Bank of America, in its efforts to build up private cloud capability, ran just south of 90,000 physical servers and 40 per cent of them were running in the cloud. BofA has set the target to create scaling and capability similar to that of Amazon Web Services environment too, according to Brad Spiers, Head of Compute Innovation at BofA. Interesting to note is that BofA is heavily investing in graphics processing capability, solid-state storage and in-memory databases, with large, fast processing and decision-making capability as the objective. NAB (previously National Australia Bank) of Australia has also committed extensively to private cloud infrastructure as it has moved to a platform-as-a-service concept as part of its programmes built around what it calls NextGen.
Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia by Anthony M. Townsend
1960s counterculture, 4chan, A Pattern Language, Airbnb, Amazon Web Services, anti-communist, Apple II, Bay Area Rapid Transit, Burning Man, business process, call centre, carbon footprint, charter city, chief data officer, clean water, cleantech, cloud computing, computer age, congestion charging, connected car, crack epidemic, crowdsourcing, DARPA: Urban Challenge, data acquisition, Deng Xiaoping, East Village, Edward Glaeser, game design, garden city movement, Geoffrey West, Santa Fe Institute, George Gilder, ghettoisation, global supply chain, Grace Hopper, Haight Ashbury, Hedy Lamarr / George Antheil, hive mind, Howard Rheingold, interchangeable parts, Internet Archive, Internet of things, Jacquard loom, Jacquard loom, Jane Jacobs, jitney, John Snow's cholera map, Khan Academy, Kibera, knowledge worker, load shedding, M-Pesa, Mark Zuckerberg, megacity, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, openstreetmap, packet switching, patent troll, place-making, planetary scale, popular electronics, RFC: Request For Comment, RFID, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart grid, smart meter, social graph, social software, social web, special economic zone, Steve Jobs, Steve Wozniak, Stuxnet, supply-chain management, technoutopianism, Ted Kaczynski, telepresence, The Death and Life of Great American Cities, too big to fail, trade route, Tyler Cowen: Great Stagnation, Upton Sinclair, uranium enrichment, urban decay, urban planning, urban renewal, Vannevar Bush, working poor, working-age population, X Prize, Y2K, zero day, Zipcar
During a scary but not very destructive earthquake on the US East Coast in the summer of 2011, cell networks were again overwhelmed. Yet media reports barely noted it. Cellular outages during crises have become so commonplace in modern urban life that we no longer question why they happen or how the problem can be fixed. Disruptions in public cloud-computing infrastructure highlight the vulnerabilities of dependence on network apps. Amazon Web Services, the eight-hundred-pound gorilla of public clouds that powers thousands of popular websites, experienced a major disruption in April 2011, lasting three days. According to a detailed report on the incident posted to the company’s website, the outage appears to have been a normal accident, to use Perrow’s term. A botched configuration change in the data center’s internal network, which had been intended to upgrade its capacity, shunted the entire facility’s traffic onto a lower-capacity backup network.
., 265 ACM Queue, 266 Adams, Sam, 83 Aerotropolis (Kasarda and Lindsay), 24 Agent.btz, 269 Airbnb, 163 air-conditioning, early solutions for, 19–20 air defense, computer systems for, 63 Air Force, U.S., 63, 259 “air-gapping,” 269 AirPort, 128 air transportation, 63 digital technology in, 32–33 Albritton, Dan, 301–2 Alexander, Christopher, 142–44, 285–86 Alfeld, Louis Edward, 81–82, 86 Allan, Alasdair, 271 Altair, MITS, 153 Altman, Anne, 65 Amar, Georges, 106, 133 Amazon Web Services, 263–64 American Airlines, 63–64 American Express, 62 Amin, Massoud, 35 Amsterdam, 279 analog cellular, 53 Angelini, Alessandro, 91–92 Ansari X PRIZE, 202–3 API (application program interface), 150 Apple, 49, 128, 148, 271 Siri of, 233 apps, 121–26, 144–52, 183, 213, 235 to address urban problems, 156–59 badges for, 148 contests for, 156, 200–205, 212, 215, 225, 227–30 for navigation of disabled, 166 situated software as, 232–36 “Trees Near You” as, 201–2 variety of, 6 Apps for Democracy, 156, 200–201, 203 Arab Spring, social media in, 11–12 Arbon, 37 Arcaute, Elsa, 313–14 Archibald, Rae, 80 Archigram, 20–21 Architectural Association (London), 20 Architectural Forum, 142 Architecture Without Architects (Rudofsky), 111–12 Arduino, 137–41 ARPA (Advanced Research Projects Agency), 259 ARPANET, 111, 259–60, 269 ArrivalStar, 293 Arup, 32 Ashlock, Philip, 158–59 Asimov, Isaac, 73–75, 88 Association for Computing Machinery, 260 Astando, 244 AT&T, 35–37, 51–52, 111, 260, 272 dial-up Internet service at, 36 Atlanta, Ga., 66 Atlantic, The, 75 AutoCAD, 302 AutoDesk, 302 automobile, as new technology, 7 Ayers, Charlie, 252 Babajob, 178–79 “Baby Bells,” 195 Baltimore, Md., 211 Banavar, Guru, 66–67, 69, 90, 306 Bangalore, 66, 178–79 Cisco’s smart city engineering group at, 45 as fast-growing city, 13 Ban Ki-moon, 181–82 Banzi, Massimo, 137 Baran, Paul, 259–60 Barcelona, 10, 246–47 destruction of wall of, 43 Barragán, Hernando, 137 Barry, Marion, 199 Batty, Michael, 85–87, 295–97, 313, 315–16 Becker, Gene, 112–13 Beijing, 49, 273–74 Belloch, Juan Alberto, 223 Beniger, James, 42–43 Bentham, Jeremy, prison design of, 13 Berlin, 38 Bernstein, Phil, 302 Bettencourt, Luis, 312–13 Betty, Garry, 196 Bhoomi, 12–13 big data, 29, 87, 191, 292–93, 297, 305–6, 316, 319 “Big Ideas from Small Places” (Khanna and Skilling), 224 BlackBerry Messenger, riots coordinated via, 12 blogosphere, 155 Bloomberg, Michael, 147, 205–6, 304 Boing-Boing, 156 Booz Allen Hamilton, 30 Bosack, Len, 44 Boston, Mass., 212–17, 239–41, 306–7 “Adopt-A-Hydrant” in, 213 Discover BPS, 240–42 Office of New Urban Mechanics in, 213–16 “What Are My Schools?”
WikiLeaks and the Age of Transparency by Micah L. Sifry
1960s counterculture, Amazon Web Services, banking crisis, barriers to entry, Bernie Sanders, Buckminster Fuller, Chelsea Manning, citizen journalism, Climategate, crowdsourcing, Google Earth, Howard Rheingold, Internet Archive, Jacob Appelbaum, Julian Assange, Network effects, RAND corporation, school vouchers, Skype, social web, Stewart Brand, web application, WikiLeaks
SIFRY 13 14 15 16 17 18 19 20 21 22 23 24 205 January 28, 2011, www.reuters.com/article/2011/01/28/us-wikileaksidUSTRE70R5A120110128?pageNumber=1. See http://about.lob.by/localeaks/ for details. Micah L. Sifry, “From WikiLeaks to OpenLeaks, Via the Knight News Challenge,” techPresident, December 17, 2010, http://techpresident.com/ blog-entry/wikileaks-openleaks-knight-news-challenge. Amazon Web Services, http://aws.amazon.com/message/65348. Interview with author, December 1, 2010. PayPal’s vice president of platform, Osama Bedier, said that the company acted in response to the State Department legal advisor’s letter to WikiLeaks, which declared its receipt of the leaked cables to be a violation of the law. PayPal’s terms of service says its payment system “cannot be used for any activities that encourage, promote, facilitate or instruct others to engage in illegal activity.”
Startup Weekend: How to Take a Company From Concept to Creation in 54 Hours by Marc Nager, Clint Nelsen, Franck Nouyrigat
Amazon Web Services, barriers to entry, business climate, invention of the steam engine, James Watt: steam engine, Mark Zuckerberg, minimum viable product, pattern recognition, Silicon Valley, transaction costs, web application, Y Combinator
The following list is by no means complete: Andy Sack, Bill Warner, Brad Feld, Dave McClure, Denis Browne, Eric Ries, Jessica Livingston, John Lewis, Jonathan Ortmans, Kathleen Kennedy, Mark Suster, Robert Scoble, and Yosi Vardi. We'd be remiss if we didn't acknowledge the huge impact our global sponsors have had not only on the organization but on the thousands of Startup Weekend alumni, too. Thank you to Amazon Web Services (Rodica Buzescu), oDesk, O'Reilly, Microsoft BizSpark (particularly Juliano Tubino, Julien Codiniou, Ludo Ulrich, and the rest of the global team), Sun Microsystems (particularly Jeremiah Shackelford), TokBox, and Twilio. Another round of thanks is also in order for the regional and local sponsors who help us bring our events to cities around the world. A huge thank you goes out to the Startup Weekend Core Team: Keith Armstrong, Jennifer Cabala, Anca Foster, Ashley Hodgson, Maris McEdward, Joey Pomerenke, Tawnee Rebhuhn, Shane Reiser, and Adam Stelle for their belief in and commitment to our vision.
The Automatic Customer: Creating a Subscription Business in Any Industry by John Warrillow
Airbnb, airport security, Amazon Web Services, asset allocation, barriers to entry, call centre, cloud computing, discounted cash flows, high net worth, Jeff Bezos, Network effects, passive income, rolodex, sharing economy, side project, Silicon Valley, Silicon Valley startup, software as a service, statistical model, Steve Jobs, Stewart Brand, subscription business, telemarketer, time value of money, Zipcar
Not surprisingly, its first focus was on baby products like diapers and wipes—a category Amazon placed a big bet on when it paid $545 million to acquire Quidsi, the creators of Diapers.com, which itself offers a subscription for diapers that enjoyed 30% month-over-month growth in 2013.6 Amazon is known for its wins in selling to consumers—but subscriptions can work for B2B as well as B2C. One of Amazon’s latest ventures is a subscription that offers to help other companies grow their subscription businesses. Amazon Web Services (AWS) offers companies access to servers, software, and technology support on a subscription basis. Many of the world’s largest subscription companies, including Adobe, Citrix, Netflix, and Sage, use AWS, along with many of the highest-profile start-ups, like Airbnb, Pinterest, Dropbox, and Spotify. Amazon is pioneering the subscription model in virtually every area of its business, but the subscription model is nothing new.
Graph Databases by Ian Robinson, Jim Webber, Emil Eifrem
Amazon Web Services, anti-pattern, bioinformatics, corporate governance, create, read, update, delete, data acquisition, en.wikipedia.org, fault tolerance, linked data, loose coupling, Network effects, recommendation engine, semantic web, sentiment analysis, social graph, software as a service, SPARQL, web application
With this strategy, writes to the cluster are buffered in a queue; a worker then polls the queue and executes batches of writes against the database. Not only does this regulate write traffic, it reduces contention, and allows you to pause write operations without refusing client requests during maintenance periods. Global clusters For applications catering to a global audience, it is possible to install a multi-region cluster in multiple datacenters and on cloud platforms such as Amazon Web Services 7. See http://docs.neo4j.org/chunked/milestone/ha-configuration.html Application Architecture | 73 (AWS). A multi-region cluster allows you to service reads from the portion of the cluster geographically closest to the client. In these situations, however, the latency introduced by the physical separation of the regions can sometimes disrupt the coordination pro‐ tocol; it is, therefore, often desirable to restrict master reelection to a single region.
Blockchain: Blueprint for a New Economy by Melanie Swan
23andMe, Airbnb, altcoin, Amazon Web Services, asset allocation, banking crisis, bioinformatics, bitcoin, blockchain, capital controls, cellular automata, central bank independence, clean water, cloud computing, collaborative editing, Conway's Game of Life, crowdsourcing, cryptocurrency, disintermediation, Edward Snowden, en.wikipedia.org, ethereum blockchain, fault tolerance, fiat currency, financial innovation, Firefox, friendly AI, Hernando de Soto, Internet Archive, Internet of things, Khan Academy, Kickstarter, litecoin, Lyft, M-Pesa, microbiome, Network effects, new economy, peer-to-peer lending, personalized medicine, post scarcity, prediction markets, ride hailing / ride sharing, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, SETI@home, sharing economy, Skype, smart cities, smart contracts, smart grid, software as a service, technological singularity, Turing complete, unbanked and underbanked, underbanked, web application, WikiLeaks
Genomecoin, GenomicResearchcoin Even without considering the longer-term speculative possibilities of the complete invention of an industrial-scale all-human genome sequencing project with the blockchain, just adding blockchain technology as a feature to existing sequencing activities could be enabling. Conceptually, this would be like adding coin functionality or blockchain functionality to services like DNAnexus, a whole-human genome cloud-based storage service. Operating in collaboration with university collaborators (Baylor College of Medicine’s Human Genome Sequencing Center) and Amazon Web Services, the DNAnexus solution is perhaps the largest current data store of genomes, having 3,751 whole human genomes and 10,771 exomes (440 terabytes) as of 2013.147 The progress to date is producing a repository of 4,000 human genomes, out of the possible field of 7 billion humans, which highlights the need for large-scale models in these kinds of big data projects (human whole-genome sequencing).
Amazon Web Services, clean water, cloud computing, computer vision, en.wikipedia.org, full employment, income inequality, job automation, knowledge worker, mutually assured destruction, Occupy movement, Search for Extraterrestrial Intelligence, self-driving car, Stephen Hawking, working poor
No one in 2000 would have imagined a company like Google with control over millions of server machines and petabytes of storage space. Yet here we are in 2015 and Google exists in that form. Many other companies – Microsoft, Facebook, Apple, Amazon, etc. - have similar amounts of computing power and storage space. And Amazon makes it possible for anyone to easily build their own cloud platform using a system called AWS (Amazon Web Services). The processing power, hard disk space and RAM in a typical desktop computer has increased dramatically because of Moore's Law since desktop machines first appeared in the 1980s. Extrapolating out to the years 2020 and 2040 shows a startling increase in computer power. The point where small, inexpensive computers have power approaching that of the human brain is just a few decades away.
The Silent Intelligence: The Internet of Things by Daniel Kellmereit, Daniel Obodovski
3D printing, Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, connected car, crowdsourcing, data acquisition, en.wikipedia.org, Erik Brynjolfsson, first square of the chessboard, first square of the chessboard / second half of the chessboard, Freestyle chess, Google X / Alphabet X, Internet of things, Network effects, Paul Graham, Ray Kurzweil, RFID, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, web application, Y Combinator, yield management
Most companies providing asset tracking and monitoring services do it in a SaaS way, again bypassing the IT departments and providing much more cost-effective and accurate solutions that way. But speaking of security of connectivity and information, Sanjay Sarma believes cloud services are not any less secure than the ones provided by corporate IT: I think that outsourcing innovation actually ensures security, because you have a few professionals that have best practices. I’d rather trust a thousand people at a company like Amazon Web Services than the four guys in an IT division who have been working there for twenty years. The cloud companies will have the latest enterprise software on their machines, including the latest security updates. That is particularly true if you are doing certain things with sensors that would generate gazillions of terabytes, so much data to be interpreted, analyzed, etc. Indeed, internal IT departments might not have capacity and capability to deal with these volumes of data.
The Fourth Industrial Revolution by Klaus Schwab
3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, Buckminster Fuller, call centre, clean water, collaborative consumption, conceptual framework, continuous integration, crowdsourcing, disintermediation, distributed ledger, Edward Snowden, Elon Musk, epigenetics, Erik Brynjolfsson, future of work, global value chain, Google Glasses, income inequality, Internet Archive, Internet of things, invention of the steam engine, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, life extension, Lyft, megacity, meta analysis, meta-analysis, more computing power than Apollo, mutually assured destruction, Narrative Science, Network effects, Nicholas Carr, personalized medicine, precariat, precision agriculture, Productivity paradox, race to the bottom, randomized controlled trial, reshoring, RFID, rising living standards, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, smart cities, smart contracts, software as a service, Stephen Hawking, Steve Jobs, Steven Levy, Stuxnet, The Spirit Level, total factor productivity, transaction costs, Uber and Lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, winner-take-all economy, women in the workforce, working-age population, Y Combinator, Zipcar
One reason for it is that the storage price (Figure IV) has dropped exponentially (by a factor of approximately ten, every five years). Figure IV: Hard Drive Cost per Gigabyte (1980-2009) Source: “a history of storage costs”, mkomo.com, 8 September 200988 An estimated 90% of the world’s data has been created in the past two years, and the amount of information created by businesses is doubling every 1.2 years.89 Storage has already become a commodity, with companies like Amazon Web Services and Dropbox leading this trend. The world is heading towards a full commoditization of storage, through free and unlimited access for users. The best-case scenario of revenue for companies could potentially be advertising or telemetry. Positive impacts – Legal systems – History scholarship/academia – Efficiency in business operations – Extension of personal memory limitations Negative impact – Privacy surveillance Unknown, or cuts both ways – Eternal memory (nothing deleted) – Increased content creation, sharing and consumption The shift in action Numerous companies already offer free storage in the cloud, ranging from 2 GB to 50 GB.
The Filter Bubble: What the Internet Is Hiding From You by Eli Pariser
A Declaration of the Independence of Cyberspace, A Pattern Language, Amazon Web Services, augmented reality, back-to-the-land, Black Swan, borderless world, Build a better mousetrap, Cass Sunstein, citizen journalism, cloud computing, cognitive dissonance, crowdsourcing, Danny Hillis, data acquisition, disintermediation, don't be evil, Filter Bubble, Flash crash, fundamental attribution error, global village, Haight Ashbury, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, Mark Zuckerberg, Marshall McLuhan, megacity, Netflix Prize, new economy, PageRank, paypal mafia, Peter Thiel, recommendation engine, RFID, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, social graph, social software, social web, speech recognition, Startup school, statistical model, stem cell, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, the scientific method, urban planning, Whole Earth Catalog, WikiLeaks, Y Combinator
Rather than housing their Web sites and databases internally, many businesses and start-ups now run on virtual computers in vast server farms managed by other companies. The enormous pool of computing power and storage these networked machines create is known as the cloud, and it allows clients much greater flexibility. If your business runs in the cloud, you don’t need to buy more hardware when your processing demands expand: You just rent a greater portion of the cloud. Amazon Web Services, one of the major players in the space, hosts thousands of Web sites and Web servers and undoubtedly stores the personal data of millions. On one hand, the cloud gives every kid in his or her basement access to nearly unlimited computing power to quickly scale up a new online service. On the other, as Clive Thompson pointed out to me, the cloud “is actually just a handful of companies.” When Amazon booted the activist Web site WikiLeaks off its servers under political pressure in 2010, the site immediately collapsed—there was nowhere to go.
Web Scraping With Python: Collecting Data From the Modern Web by Ryan Mitchell
AltaVista, Amazon Web Services, cloud computing, en.wikipedia.org, Firefox, meta analysis, meta-analysis, natural language processing, optical character recognition, random walk, self-driving car, Turing test, web application
Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Brian Krebs, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration
John Markoff, “Researchers Announce Advance in Image-Recognition Software,” New York Times, November 17, 2014, science section. 5. “Strawberry Harvesting Robot,” posted by meminsider, YouTube, November 30, 2010, http://youtu.be/uef6ayK8ilY. 6. For an amazingly insightful analysis of the effects of increased communication and decreased energy cost across everything from living cells to civilizations, see Robert Wright, Nonzero (New York: Pantheon 2000). 7. Amazon Web Services (AWS), accessed November 25, 2014, http://aws.amazon.com. 8. W. B. Yeats, “The Second Coming,” 1919, http://en.wikipedia.org/wiki/The_Second_Coming_(poem). 3. ROBOTIC PICKPOCKETS 1. At least, that’s the way I remember it. Dave may have a different recollection, especially in light of the fact that Raiders wasn’t released until 1981. 2. David Elliot Shaw, “Evolution of the NON-VON Supercomputer,” Columbia University Computer Science Technical Reports, 1983, http://hdl.handle.net/10022/AC:P:11591. 3. http://en.wikipedia.org/wiki/MapReduce, last modified December 31, 2014. 4.
3D printing, Amazon Web Services, augmented reality, call centre, clockwatching, cloud computing, Firefox, future of work, ghettoisation, Google Chrome, Google Glasses, Google Hangouts, Khan Academy, Kickstarter, Kodak vs Instagram, Lean Startup, Mark Zuckerberg, Network effects, new economy, Occupy movement, place-making, prediction markets, pre–internet, recommendation engine, Richard Florida, risk tolerance, self-driving car, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, Steve Jobs, Steve Wozniak, Thomas L Friedman, Tim Cook: Apple, Tony Hsieh, WikiLeaks
Google continues to fascinate as the search engine expands into areas like online video (YouTube), mobile (Android and the Nexus line of devices), email services (Gmail), Web browsers (Google Chrome), online social networking (Google+), and beyond (self-driving cars and Google Glasses). Amazon continues to squiggle by pushing beyond selling books online into e-readers (Kindle), selling shoes (Zappos), offering cloud computing technology (Amazon Web Services), and beyond. When you actually start digging down deep into how these companies have evolved and stayed relevant, you won’t see business models that look like anything from the playbooks of Kodak or RIM. These organizations are in a constant state of rebooting with teams of people who are actively guiding their own careers as they squiggle. Even MySpace is making another run at it by being squiggly.
Effective Programming: More Than Writing Code by Jeff Atwood
AltaVista, Amazon Web Services, barriers to entry, cloud computing, endowment effect, Firefox, future of work, game design, Google Chrome, gravity well, job satisfaction, Khan Academy, Kickstarter, loss aversion, Mark Zuckerberg, Merlin Mann, Minecraft, Paul Buchheit, Paul Graham, price anchoring, race to the bottom, recommendation engine, science of happiness, Skype, social software, Steve Jobs, web application, Y Combinator
Software developers should share the customer’s pain. I know it’s not glamorous. But until you’ve demonstrated a willingness to help the customers using the software you’ve built — and more importantly, learn why they need help — you haven’t truly finished building that software. Working With the Chaos Monkey Late last year, the Netflix Tech Blog wrote about five lessons they learned moving to Amazon Web Services. AWS is, of course, the preeminent provider of so-called “cloud computing,” so this can essentially be read as key advice for any website considering a move to the cloud. And it’s great advice, too. Here’s the one bit that struck me as most essential: We’ve sometimes referred to the Netflix software architecture in AWS as our Rambo Architecture. Each system has to be able to succeed, no matter what, even all on its own.
3D printing, Airbnb, Amazon Web Services, barriers to entry, bitcoin, blockchain, business process, Clayton Christensen, collaborative economy, crowdsourcing, cryptocurrency, data acquisition, frictionless, game design, hive mind, Internet of things, invisible hand, Kickstarter, Lean Startup, Lyft, M-Pesa, Mark Zuckerberg, means of production, multi-sided market, Network effects, new economy, Paul Graham, recommendation engine, ride hailing / ride sharing, shareholder value, sharing economy, Silicon Valley, Skype, Snapchat, social graph, social software, software as a service, software is eating the world, Spread Networks laid a new fibre optics cable between New York and Chicago, TaskRabbit, the payments system, too big to fail, transport as a service, two-sided market, Uber and Lyft, Uber for X, Wave and Pay
Uber offers benefits and vehicle purchase schemes to drivers who want to buy a new car and start participating on Uber. THE RESOURCE BARRIER It is much easier to start a business in 2015 than it was in 1995. One of the most important reasons is the significant reduction in the amount of resources required to get a business up and running. An important contributor to this change is the rise of Amazon Web Services, which lowers the amount of resources required at the outset to start up. A startup that would have needed to procure a minimum level of infrastructure in 1995 may leverage Amazon’s resources on demand in 2015. THE ACCESS BARRIER Platforms often disrupt gatekeepers by giving producers direct access to potential consumers. Most media businesses (publishing, performing arts) are industries with gatekeepers that determine which producers get market access.
3D printing, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple's 1984 Super Bowl advert, barriers to entry, Berlin Wall, big-box store, bitcoin, business climate, call centre, Cass Sunstein, centralized clearinghouse, Chelsea Manning, citizen journalism, cloud computing, collaborative consumption, collaborative editing, crony capitalism, cross-subsidies, crowdsourcing, David Brooks, death of newspapers, Donald Trump, Douglas Engelbart, en.wikipedia.org, Exxon Valdez, Fall of the Berlin Wall, Filter Bubble, Firefox, Galaxy Zoo, global supply chain, Google Chrome, Gordon Gekko, Hacker Ethic, Jaron Lanier, Jeff Bezos, jimmy wales, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, Lean Startup, Mark Zuckerberg, minimum viable product, Mohammed Bouazizi, Mother of all demos, Narrative Science, new economy, Occupy movement, Peter Thiel, pirate software, Ronald Reagan, Ronald Reagan: Tear down this wall, sharing economy, Silicon Valley, Skype, social web, Steve Jobs, Steve Wozniak, Stewart Brand, Stuxnet, Ted Nelson, Telecommunications Act of 1996, telemarketer, The Wisdom of Crowds, transaction costs, uranium enrichment, Whole Earth Catalog, WikiLeaks, Zipcar
Of course, when you’re surfing the Internet, you don’t care where the Web sites physically reside. You’re in the virtual cloud of the Internet, and the specific server—be it in Idaho, New York, or Shanghai—doesn’t affect your experience. Indeed, servers have become incredibly commoditized, with large volumes of computing power made available in seconds for pennies. Amazon has developed some notoriety in this area with a product called Amazon web services (AWS). In the process of building a giant infrastructure to sell everything, but especially books, over the Internet, Amazon realized that they could sell excess capacity on their server farms. Need a place to host your Web site? Buy it from Amazon. Suddenly need 10,000 times more space because your tiny start-up has gone viral? Amazon can turn it on in seconds. Hundreds if not thousands of vendors now offer cloud computing.
Clojure Programming by Chas Emerick, Brian Carper, Christophe Grand
Amazon Web Services, Benoit Mandelbrot, cloud computing, continuous integration, database schema, domain-specific language, en.wikipedia.org, failed state, finite state, Firefox, game design, general-purpose programming language, mandelbrot fractal, Paul Graham, platform as a service, premature optimization, random walk, Schrödinger's Cat, semantic web, software as a service, sorting algorithm, Turing complete, type inference, web application
We’ll take a look at one, Amazon’s Elastic Beanstalk service, that is broadly applicable to Clojure web applications, automating the provisioning and configuration of servers and deployment of applications to those servers. Deploying Clojure Apps to Amazon’s Elastic Beanstalk Amazon’s Elastic Beanstalk (EB) is a platform as a service that provides a thin layer of automation and deployment management tools on top of Amazon Web Services’s (AWS) lower-level EC2 compute and load balancer services. EB allows you to programmatically provision and control environments (collections of one or more application servers fronted by a load balancer), to which you can deploy different versions of your application. The load balancers used by EB are integrated with this provisioning mechanism, so that when your application experiences higher load (based on metrics you define, such as number of requests or aggregate bandwidth utilized per minute), the corresponding EB environment is expanded to contain more app servers to service that load.
Clojure on Heroku While Chapter 17 presented a very typical web application deployment approach using a container (such as Tomcat on Amazon’s Elastic Beanstalk), there are ways to run Clojure web apps without a container, and therefore without the packaging work that containers imply. While such containerless deployment options are a relatively new approach in the JVM space, they are becoming more and more common. One of the most popular to date is provided by Heroku (http://heroku.com)—a scalable application deployment platform that is itself hosted on Amazon Web Services—which now directly supports the deployment of Ring-based web applications using Leiningen without requiring any separate compilation or packaging steps. Heroku has the added benefit of offering application “add-ons”—managed database clusters, message queues, web services, and so on—that you can configure and use from within your Clojure project without having to set up and manage such things yourself.
Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations by Thomas L. Friedman
3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, bitcoin, blockchain, business process, call centre, centre right, Clayton Christensen, clean water, cloud computing, corporate social responsibility, crowdsourcing, David Brooks, demand response, demographic dividend, demographic transition, Deng Xiaoping, Donald Trump, Erik Brynjolfsson, failed state, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, game design, gig economy, global supply chain, illegal immigration, immigration reform, income inequality, indoor plumbing, Internet of things, invention of the steam engine, inventory management, Jeff Bezos, job automation, John von Neumann, Khan Academy, Kickstarter, knowledge economy, knowledge worker, land tenure, linear programming, low skilled workers, Lyft, Mark Zuckerberg, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, pattern recognition, planetary scale, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, Steve Jobs, TaskRabbit, Thomas L Friedman, transaction costs, Transnistria, urban decay, urban planning, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y2K, Yogi Berra
Indeed, these digital flows have become so rich and powerful they are to the twenty-first century what rivers running off mountains were to civilization and cities in days of old. Back then, you wanted to build your town or your factory along a rushing river—such as the Amazon—and let it flow through you. That river would give you power, mobility, nourishment, and access to neighbors and their ideas. So it is with these digital flows into and out of the supernova. But the rivers you want to build on now are Amazon Web Services or Microsoft’s Azure—giant connectors that enable you, your business, or your nation to get access to all the computing-power applications in the supernova, where you can tie into every flow in the world in which you want to participate. The world cannot get this connected in so many new realms at such profound new depths without being reshaped. And this chapter is about how these digital global flows are doing just that: enabling so many more people around the world to access the supernova’s technology toolbox to become makers and breakers; making the world so much more interdependent in financial terms, so every country is now more vulnerable to every other country’s economy; driving contact between strangers at a pace and scale we’ve never seen before, so that good and bad ideas can go viral and extinguish and manufacture prejudices much more quickly; making every leader more exposed and transparent; and ensuring that the price countries pay for adventures abroad will be much higher than they expect, making these flows a new source of geopolitical restraint.
Louis Park Agadez, Niger age of accelerations; dislocation and; education and; human adaptability as challenged by; as inflection point; innovation as response to; leadership and; the Machine and; Moore’s law and; social technologies and agriculture: in Africa and Middle East; climate change and; monocultures vs. polycultures in Airbnb; trust and air-conditioning Aita, Samir algorithms; human oversight and; self-improving Alivio Capital Allen, Paul Allisam, Graham Almaniq, Mati Al Qaeda Al-Shabab AltaVista Amazon (company) Amazon rain forest Amazon Web Services American Civil Liberties Union American Dream American Interest American University of Iraq “America’s New Immigrant Entrepreneurs: Then and Now” (Kauffman Foundation) Amman, Jordan amplifying, as geopolitical policy Andersen, Jeanne Anderson, Chris Anderson, Ross Anderson, Wendell Andreessen, Marc Andrews, Garrett Android AngularJS Annan, Kofi Anthropocene epoch Anthropocene Review anti-Semitism APIs (application programming interfaces) Apple; see also Jobs, Steve Applebaum, Anne Apple Newton Apple Pay apps revolution Arab Awakening Arabic, author’s study of Arab-Muslim world, golden age of Arafat, Yasser architects, software for Armstrong, Neil artificial intelligence (AI); intelligent algorithms and; intelligent assistance and Artnet.com Ashe, Neil Ashraf, Quamrul Assad, Bashar al- Associated Press Astren, Fred AT&T; intelligent assistance and; iPhone gamble of; lifelong learning and; as software company Atkinson, Karen atmosphere: aerosol loading in; CO2 in; ozone layer of ATMs Auguste, Byron Austria Austro-Hungarian Empire Autodesk automation, see computers, computing autonomous systems; see also cars, self-driving Autor, David Avaaz.org Azmar Mountain Bajpai, Aloke Baker, James A., III balance of power Bandar Mahshahr, Iran bandwidth Bangladesh bankruptcy laws bank tellers Barbut, Monique baseball, class-mixing and BASIC Bass, Carl Batman, Turkey BBCNews.com Bee, Samantha Beinhocker, Eric Beirut: civil war in; 1982 Israeli-Palestinian war in Bell, Alexander Graham Bell Labs Bennis, Warren Benyus, Janine Berenberg, Morrie Berenberg, Tess Berkus, Nate Berlin, Isaiah Berlin Wall, fall of Bessen, James Betsiboka River “Better Outcomes Through Radical Inclusion” (Wells) Between Debt and the Devil (Turner) Beykpour, Kayvon Bible Bigbelly garbage cans big data; consumers and; financial services and; software innovation and; supernova and Big Shift Big World, Small Planet (Rockström) “Big Yellow Taxi” (song) Bingham, Marjorie bin Laden, Osama bin Yehia, Abdullah biodiversity: environmental niches and; resilience and biodiversity loss; climate change and biofuels biogeochemical flows biomass fuels biotechnology bioweapons birth control, opposition to Bitcoin black elephants Blase, Bill blockchain technology Bloomberg.com Blumenfeld, Isadore “Kid Cann” Bobby Z (Bobby Rivkin) Bodin, Wes Bohr, Mark Bojia, Ayele Z.
What Would Google Do? by Jeff Jarvis
23andMe, Amazon Mechanical Turk, Amazon Web Services, Anne Wojcicki, barriers to entry, Berlin Wall, business process, call centre, cashless society, citizen journalism, clean water, connected car, credit crunch, crowdsourcing, death of newspapers, disintermediation, diversified portfolio, don't be evil, fear of failure, Firefox, future of journalism, Google Earth, Googley, Howard Rheingold, informal economy, inventory management, Jeff Bezos, jimmy wales, Kevin Kelly, Mark Zuckerberg, moral hazard, Network effects, new economy, Nicholas Carr, PageRank, peer-to-peer lending, post scarcity, prediction markets, pre–internet, Ronald Coase, search inside the book, Silicon Valley, Skype, social graph, social software, social web, spectrum auction, speech recognition, Steve Jobs, the medium is the message, The Nature of the Firm, the payments system, The Wisdom of Crowds, transaction costs, web of trust, Y Combinator, Zipcar
He sells his retail services to other merchants, sending them customers online and taking a cut, in some cases warehousing and shipping their inventory and charging for the services. He also took the computer infrastructure he had to build and offered it to any company as a low-cost, pay-as-you-go service: computing power, storage, databases, and a mechanism for paying programmers. Countless companies now use Amazon Web Services as their backend, foregoing or at least forestalling investments in computers and software. Amazon has also created the infrastructure for an on-demand workforce called Mechanical Turk (named after a phony chess-playing automaton from 1769 that had a human chess master hidden inside). Companies post a repetitive task to be done and anyone can earn money—as little as one cent per task—by verifying the address in a picture, for example, or categorizing content.
Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement by Eric Redmond, Jim Wilson, Jim R. Wilson
Amazon Web Services, create, read, update, delete, data is the new oil, database schema, Debian, domain-specific language, en.wikipedia.org, fault tolerance, full text search, general-purpose programming language, linked data, MVC pattern, natural language processing, node package manager, random walk, recommendation engine, Skype, social graph, web application
Using cURL allows us to peek at the underlying API without resorting to a particular driver or programming language. Riak is a great choice for datacenters like Amazon that must serve many requests with low latency. If every millisecond spent waiting is a potential customer loss, Riak is hard to beat. It’s easy to manage, easy to set up, and can grow with your needs. If you’ve ever used Amazon Web Services, like SimpleDB or S3, you may notice some similarities in form and function. This is no coincidence. Riak is inspired by Amazon’s Dynamo paper. In this chapter, we’ll investigate how Riak stores and retrieves values and how to tie data together using Links. Then we’ll explore a data-retrieval concept used heavily throughout this book: mapreduce. We’ll see how Riak clusters its servers and handles requests, even in the face of server failure.
Mastering ElasticSearch by Rafal Kuc, Marek Rogozinski
In order to install it we should run the following command: bin/plugin install cloud-aws EC2 plugin's configuration This plugin provides the following configuration settings that we need to provide in order for the EC2 discover to work: cluster.aws.access_key: It is the Amazon access key, one of the credential values you can find in the Amazon configuration panel cluster.aws.secret_key: It is the Amazon secret key: similar to the previously mentioned access_key it can be found in the EC2 configuration panel The last thing is to inform ElasticSearch that we want to use new discovery type by setting the discovery.type property to the ec2 value and turn off multicast. Optional EC2 discovery configuration options The previously mentioned settings are sufficient to run the EC2 discovery, but in order to control the EC2 discovery plugin's behavior ElasticSearch exposes the following additional settings: cloud.aws.region: This specifies the region for connecting with Amazon web services. You can choose a region adequate to the region where your instance resides. For example eu-west-1 for Ireland. The possible values are: eu-west-1, us-east-1, us-west-1, and ap-southeast-1. cloud.aws.ec2.endpoint: Instead of defining a region, you can enter an address of the AWS endpoint, for example: ec2.eu-west-1.amazonaws.com. discovery.ec2.ping_timeout (default: 3s): This specifies the time to wait for the response for the ping message sent to the other node.
3D printing, AI winter, Amazon Web Services, artificial general intelligence, Automated Insights, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, cloud computing, cognitive bias, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day
In 2011, botnet victims increased 654 percent: Schwartz, Mathew, “Botnet Victims Increased 654 percent in 2011,” InformationWeek, February 18, 2011, http://www.informationweek.com/news/security/attacks/229218944?cid=RSSfeed_IWK_All (accessed July 11, 2012). a one trillion-dollar industry: Symantec, “What is Cybercrime?” last modified 2012, http://us.norton.com/cybercrime/definition.jsp (accessed July 11, 2012). Cloud computing has been a runaway success: Malik, Om, “How Big is Amazon’s Cloud Computing Business? Find Out,” GIGAOM, August 11, 2010, http://gigaom.com/cloud/amazon-web-services-revenues/ (accessed June 4, 2011). Zeus stole some $70 million: Ragan, Steve, “ZBot data dump discovered with over 74,000 FTP credentials,” The Tech Herald, June 29, 2009, http://www.thetechherald.com/articles/ZBot-data-dump-discovered-with-over-74-000-FTP-credentials/6514/ (accessed June 4, 2011). 21.3 percent overall, comes from Shaoxing: Melanson, Donald, “Symantec names Shaoxing, China, as world’s malware capital,” Engadget, March 29, 2010, http://www.engadget.com/2010/03/29/symantec-names-shaoxing-china-worlds-malware-capital (accessed June 4, 2011).
@War: The Rise of the Military-Internet Complex by Shane Harris
Amazon Web Services, barriers to entry, Berlin Wall, Brian Krebs, centralized clearinghouse, clean water, computer age, crowdsourcing, data acquisition, don't be evil, Edward Snowden, failed state, Firefox, Julian Assange, mutually assured destruction, Silicon Valley, Silicon Valley startup, Skype, Stuxnet, uranium enrichment, WikiLeaks, zero day
It is, in effect, like the top-secret networks the military uses. It won’t be impervious to assault—neither are the military’s, as the Buckshot Yankee operation showed. But they will afford a higher level of security than what you have now in the mostly ungoverned expanse of the Internet. Who would build such a community? Perhaps Amazon. In fact, it has already built a version—for the CIA. Amazon Web Services, which hosts other companies’ data and computing operations, has a $600 million contract to build a private system, or cloud, for the spy agency. But unlike other clouds, which are accessed through the public Internet, this one will be run this one using Amazon’s own hardware and network equipment. Amazon hasn’t historically offered private clouds to its customers, but the CIA may be on the frontier of a new market.
Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist
3D printing, additive manufacturing, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business process, chief data officer, cloud computing, connected car, cyber-physical system, deindustrialization, fault tolerance, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low skilled workers, millennium bug, pattern recognition, platform as a service, pre–internet, race to the bottom, RFID, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, trade route, web application, WebRTC, WebSocket, Y2K
For example, the type of product, the price, discount, and any calorific or nutrient data normally declared on the label. Knowing a user’s location, activity, and interests will enable location based services (LBS), such as instantaneously providing a coupon for a discount. The Cloud and Fog Cloud computing is similar to many technologies that have been around for decades. It really came to the fore, in the format that we now recognize, in the mid 2000s with the launch of Amazon Web Services (AWS). AWS was followed by RackSpace, Google’s CE, and Microsoft Azure, among several others. Amazon’s vision of the cloud was on hyper-provisioning; in so much as they built massive data centers with hyper-capacity in order to meet their web-scale requirements. Amazon then took the business initiative to rent spare capacity to other businesses, in the form of leasing compute, and storage resources on an as-used basis.
The Connected Company by Dave Gray, Thomas Vander Wal
A Pattern Language, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Atul Gawande, Berlin Wall, business process, call centre, Clayton Christensen, complexity theory, en.wikipedia.org, factory automation, Googley, index card, interchangeable parts, inventory management, Jeff Bezos, Kevin Kelly, loose coupling, market design, minimum viable product, more computing power than Apollo, profit maximization, Richard Florida, self-driving car, shareholder value, side project, Silicon Valley, skunkworks, software as a service, South of Market, San Francisco, Steve Jobs, Steven Levy, Stewart Brand, The Wealth of Nations by Adam Smith, Tony Hsieh, Toyota Production System, Vanguard fund, web application, WikiLeaks, Zipcar
Strategy expert Karl Moore, a colleague of Mintzberg, describes emergent strategy this way: “Let a thousand flowers bloom…lop off the heads of most of the thousand flowers, and scale up those experiments and pilots that work.” Deliberate strategy is goal-oriented. It asks, “What do we want to achieve?” Emergent strategy is means-oriented. It asks, “What is possible, with the means we have at our disposal?” You can see emergent strategy at work at Amazon, where small-scale experiments proliferate and the company “scales up” a few large-scale experiments, like Amazon Web Services, Kindle, and Amazon Marketplace. You can see it at work at Google, where every employee is encouraged to spend one day a week pursuing experiments of their own making, and a few of the most successful ones, like Google News and Gmail, attract resources and scale to become significant new sources of revenue. A Portfolio of Experiments Diversity breeds creativity—ecosystems are richest where habitats and species overlap.
3D printing, A Declaration of the Independence of Cyberspace, AI winter, Airbnb, Albert Einstein, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, computer age, connected car, crowdsourcing, dark matter, dematerialisation, Downton Abbey, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, game design, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Kickstarter, linked data, Lyft, M-Pesa, Marshall McLuhan, means of production, megacity, Minecraft, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, recommendation engine, RFID, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, the scientific method, transport as a service, two-sided market, Uber for X, Watson beat the top human players on Jeopardy!, Whole Earth Review
At the end of several hours of first playing a game, with no coaching from the DeepMind creators, the algorithms, called deep reinforcement machine learning, could beat humans in half of the 49 Atari video games they mastered. AIs like this one are getting smarter every month, unlike human players. Amid all this activity, a picture of our AI future is coming into view, and it is not the HAL 9000—a discrete machine animated by a charismatic (yet potentially homicidal) humanlike consciousness—or a Singularitan rapture of superintelligence. The AI on the horizon looks more like Amazon Web Services—cheap, reliable, industrial-grade digital smartness running behind everything, and almost invisible except when it blinks off. This common utility will serve you as much IQ as you want but no more than you need. You’ll simply plug into the grid and get AI as if it was electricity. It will enliven inert objects, much as electricity did more than a century past. Three generations ago, many a tinkerer struck it rich by taking a tool and making an electric version.
3D printing, Airbnb, Amazon Web Services, Andy Kessler, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business climate, call centre, car-free, cloud computing, collaborative consumption, collaborative economy, collective bargaining, congestion charging, crowdsourcing, cryptocurrency, decarbonisation, don't be evil, Elon Musk, en.wikipedia.org, ethereum blockchain, Ferguson, Missouri, Firefox, frictionless, Gini coefficient, hive mind, income inequality, index fund, informal economy, Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Lean Startup, Lyft, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, openstreetmap, optical character recognition, pattern recognition, peer-to-peer lending, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, smart cities, smart grid, Snapchat, sovereign wealth fund, Steve Crocker, Steve Jobs, Steven Levy, TaskRabbit, The Death and Life of Great American Cities, The Nature of the Firm, transaction costs, Turing test, Uber and Lyft, Zipcar
It turns out that only one independent engineer earning a mere $20,000 a year had been working on the OpenSSL code, and donations financing this key piece of Internet security amounted to only about $2,000 a year. On April 23, Jim Zemlin, the executive director of the Linux Foundation, spent all night calling potential donor companies, and by April 24 he had the funding to announce the creation of the Core Infrastructure Initiative. Thirteen companies—Amazon Web Services, Cisco Systems, Dell, Facebook, Fujitsu, Google, IBM, Intel, Microsoft, NetApp, Rackspace, Qualcomm, and VMware—together committed more than $1 million per year for three years to resolve the funding gap faced by the OpenSSL FOSS team.18 The private sector had stepped in to finance the ongoing improvement of a free and open-source platform. Another example of private money following on a community-built platform without corrupting it is GitHub.
Solr 1.4 Enterprise Search Server by David Smiley, Eric Pugh
Amazon Web Services, bioinformatics, cloud computing, continuous integration, database schema, domain-specific language, en.wikipedia.org, fault tolerance, Firefox, information retrieval, Internet Archive, web application, Y Combinator
If you were doing real world testing, then you might want to record/playback actual search requests. [ 273 ] Download at Boykma.Com This material is copyright and is licensed for the sole use by William Anderson on 26th August 2009 4310 E Conway Dr. NW, , Atlanta, , 30327 Scaling Solr Firing up Solr on Amazon EC2 In order to start using Solr on Amazon EC2, we assume that you have set up an account with Amazon Web Services, available from http://aws.amazon.com/. Firstly, we use the AWS Management Console web app at https://console.aws. amazon.com to create a Security Group for Solr that opens up ports, which the Solr master and slaves will communicate over. Just click on Security Groups and create a new Solr security group with ports 8983 through 8999 open: Then click on AMIs and search for solr-packtpub/solrbook to find the Amazon Machine Instance (AMI) prepared for this book.
Hacker, Hoaxer, Whistleblower, Spy: The Story of Anonymous by Gabriella Coleman
1960s counterculture, 4chan, Amazon Web Services, Bay Area Rapid Transit, bitcoin, Chelsea Manning, citizen journalism, cloud computing, collective bargaining, corporate governance, crowdsourcing, David Graeber, Debian, East Village, Edward Snowden, feminist movement, hive mind, impulse control, Jacob Appelbaum, jimmy wales, Julian Assange, Mohammed Bouazizi, Network effects, Occupy movement, pirate software, Richard Stallman, SETI@home, side project, Silicon Valley, Skype, Steven Levy, WikiLeaks, zero day
The typical Anonymous DDoS attacks, or “traffic floods,” were unsuccessful against service sites that perform a lot of data transactions and are served by CDNs (Content Delivery Networks) like Amazon.com. (AnonOps briefly tried to target Amazon.com directly and it was a spectacular failure.) Even with the estimated thousands of individuals contributing their computers to a voluntary botnet, their efforts never shuttered infrastructural backbones like Amazon Web Services. Anonymous’s DDoS campaigns tended to be more successful against informational sites like mpaa.org. Anonymous’s digital protest tactics essentially blocked access to these domains, but only their internet-facing websites. Given what transpires during a DDoS attack, and whatever one might think of the risks and seriousness of it, one thing seems certain: the charges levelled again Anonymous participants in the US and the UK tend to be out of line with the nonviolent nature of these actions.
The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind
23andMe, 3D printing, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, computer age, computer vision, conceptual framework, corporate governance, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, lump of labour, Marshall McLuhan, Narrative Science, natural language processing, Network effects, optical character recognition, personalized medicine, pre–internet, Ray Kurzweil, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, semantic web, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, telepresence, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, transaction costs, Turing test, Watson beat the top human players on Jeopardy!, young professional
There is an overlap here with mass collaboration, but ‘crowdsourcing’ tends to be the term used when a given project is well bounded in scope and likely time-scale, and there is a clear commission or invitation from an individual or institution for the contributions of others.96 There are many businesses that specialize in crowdsourcing. At CrowdFlower, for example, it is said that an online workforce of millions of people can be drawn from to put together teams to clean up incomplete and messy bodies of data, while at Mechanical Turk, an Amazon Web Service, requests can be sent out to Internet users to offer support for tasks that computers are currently unable to do. At Watsi, the cost of raising money to pay for healthcare is shared out among donors. The apparently selfless nature of this socializing, sharing, community-building, and co-operating can be surprising. On the face of it, there is a spirit of co-operation here that defies popular thinking about human nature, which assumes that people are predominately self-regarding, if not selfish.
HBase: The Definitive Guide by Lars George
Amazon Web Services, bioinformatics, create, read, update, delete, Debian, distributed revision control, domain-specific language, en.wikipedia.org, fault tolerance, Firefox, Google Earth, place-making, revision control, smart grid, web application
Foreword Michael Stack, HBase Project Janitor The HBase story begins in 2006, when the San Francisco-based startup Powerset was trying to build a natural language search engine for the Web. Their indexing pipeline was an involved multistep process that produced an index about two orders of magnitude larger, on average, than your standard term-based index. The datastore that they’d built on top of the then nascent Amazon Web Services to hold the index intermediaries and the webcrawl was buckling under the load (Ring. Ring. “Hello! This is AWS. Whatever you are running, please turn it off!”). They were looking for an alternative. The Google BigTable paper had just been published. Chad Walters, Powerset’s head of engineering at the time, reflects back on the experience as follows: Building an open source system to run on top of Hadoop’s Distributed Filesystem (HDFS) in much the same way that BigTable ran on top of the Google File System seemed like a good approach because: 1) it was a proven scalable architecture; 2) we could leverage existing work on Hadoop’s HDFS; and 3) we could both contribute to and get additional leverage from the growing Hadoop ecosystem.