Today (12th March, 2018) is the World Wide Web’s 29th birthday. Sir Tim Berners-Lee (the “inventor of the world-wide web”), in an interview with the Financial Times and in this Web Foundation post has used this anniversary to raise awareness of how the web behemoths Facebook, Google and Twitter are “promoting misinformation and ‘questionable’ political advertising while exploiting people’s personal data”. Whilst I admire hugely Tim Berners-Lee’s universe-denting invention it has to be said he himself is not entirely without fault in the wayhe bequeathed us with his invention. In his defence, hindsight is a wonderful thing of course, no one could have possibly predicted at the time just how the web would take off and transform our lives both for better and for worse.
If, as Marc Andreessen famously said in 2011, software is eating the world then many of those powerful tech companies are consuming us (or at least our data and I’m increasingly becoming unsure there is any difference between us and the data we choose to represent ourselves by.
Here are five recent examples of some of the negative ways software is eating up our world.
Over the past 40+ years the computer software industry has undergone some fairly major changes. Individually these were significant (to those of us in the industry at least) but if we look at these changes with the benefit of hindsight we can see how they have combined to bring us to where we are today. A world of cheap, ubiquitous computing that has unleashed seismic shocks of disruption which are overthrowing not just whole industries but our lives and the way our industrialised society functions. Here are some highlights for the 40 years between 1976 and 2016.
I have written before about how I believe that we, as software architects, have a responsibility, not only to explain the benefits (and there are many) of what we do but also to highlight the potential negative impacts of software’s voracious appetite to eat up our world.
This is my 201st post on Software Architecture Zen (2016/17 were barren years in terms of updates). This year I plan to spend more time examining some of the issues raised in this post and look at ways we can become more aware of them and hopefully not become so seduced by those sirenic entrepreneurs.
I’ve recently been spending a fair bit of time in hospital. Not, thankfully, for myself but with my mother who fell and broke her arm a few weeks back which has resulted in lots of visits to our local Accident & Emergency (A&E) department as well as a short stay in hospital whilst they pinned her arm back in place.
Anyone who knows anything about the UK also knows how much we value our National Health Service (NHS). So much so that when it was our turn to run the Olympic Games back in 2012 Danny Boyle’s magnificent opening ceremony dedicated a whole segment to this wonderful institution featuring doctors, nurses and patients dancing around beds to music from Mike Oldfield’s Tubular Bells.
The NHS was created out of the ideal that good healthcare should be available to all, regardless of wealth. When it was launched by the then minister of health, Aneurin Bevan, on July 5 1948, it was based on three core principles:
that it meet the needs of everyone
that it be free at the point of delivery
that it be based on clinical need, not ability to pay
These three principles have guided the development of the NHS over more than 60 years, remain at its core and are embodied in its constitution.
NHS net expenditure (resource plus capital, minus depreciation) has increased from £64.173 billion in 2003/04 to £113.300bn in 2014/15. Planned expenditure for 2015/16 is £116.574bn.
Health expenditure (medical services, health research, central and other health services) per capita in England has risen from £1,841 in 2009/10 to £1,994 in 2013/14.
The NHS net deficit for the 2014/15 financial year was £471 million (£372m underspend by commissioners and a £843m deficit for trusts and foundation trusts).
Current expenditure per capita for the UK was $3,235 in 2013. This can be compared to $8,713 in the USA, $5,131 in the Netherlands, $4,819 in Germany, $4,553 in Denmark, $4,351 in Canada, $4,124 in France and $3,077 in Italy.
The NHS also happens to be the largest employer in the UK. In 2014 the NHS employed 150,273 doctors, 377,191 qualified nursing staff, 155,960 qualified scientific, therapeutic and technical staff and 37,078 managers.
So does it work?
From my recent experience I can honestly say yes. Whilst it may not be the most efficient service in the world the doctors and nurses managed to fix my mothers arm and hopefully set her on the road to recovery. There have been, and I’m sure there will be more, setbacks but given her age (she is 90) they have done an amazing job.
Whilst sitting in those A&E departments whiling away the hours (I did say they could be more efficient) I had plenty of time to observe and think. By its very nature the health service is hugely people intensive. Whilst there is an amazing array of machines beeping and chirping away most activities require people and people cost money.
The UK’s health service, like that of nearly all Western countries, is under a huge amount of pressure:
The UK population is projected to increase from an estimated 63.7 million in mid-2012 to 67.13 million by 2020 and 71.04 million by 2030.
The UK population is expected to continue ageing, with the average age rising from 39.7 in 2012 to 42.8 by 2037.
The number of people aged 65 and over is projected to increase from 10.84m in 2012 to 17.79m by 2037. The number of over-85s is estimated to more than double from 1.44 million in 2012 to 3.64 million by 2037.
The number of people of State Pension Age (SPA) in the UK exceeded the number of children for the first time in 2007 and by 2012 the disparity had reached 0.5 million (though this is projected to reverse by).
There are an estimated 3.2 million people with diabetes in the UK (2013). This is predicted to reach 4 million by 2025.
In England the proportion of men classified as obese increased from 13.2 per cent in 1993 to 26.0 per cent in 2013 (peak of 26.2 in 2010), and from 16.4 per cent to 23.8 per cent for women over the same timescale (peak of 26.1 in 2010).
The doctors and nurses that looked after my mum so well are going to be coming under a increasing pressures as this ageing and less healthy population begins to suck ever more resources out of an already stretched system. So why, given the passion everyone has about the NHS, isn’t there more of a focus on getting technology to ease the burden of these overworked healthcare providers?
Part of the problem of course is that historically the tech industry hasn’t exactly covered itself with glory when it comes to delivering technology to the healthcare sector (I’m thinking the NHS National Programme for IT and the US HealthCare.gov system as being two high profile examples). Whilst some of this may be due to the blunders of government much of it is down to a combination of factors caused by both the providers and consumers of healthcare IT mis-communication and not understanding the real requirements that such complex systems tend to have.
Have a clear monetization strategy and understand your customers’ willingness-to-pay.
Know the rules and regulations.
Figure out what your unfair competitive advantage is.
Of course, these are strategies that actually apply to any industry when trying to bring about innovation and disruption – they are not unique to healthcare. I would say that when it comes to the healthcare industry the reason why there has been no Uber is because the tech industry is ignoring the generation that is in most need of benefiting from technology, namely the post 65 age group. This is the age group that struggle most with technology either because they are more likely to be digitally disadvantaged or because they simply find it too difficult to get to grips with it.
“Venture capitalists are too busy investing in Uber and things that get virality. The reality is that selling to older people is harder, and if venture capitalists detect resistance, they don’t invest.”
Matters are not helped by the fact that most tech entrepreneurs are between the ages of 20 and 35 and have different interests in life than the problems faced by the aged. As this article by Kevin Maney in the Independent points out:
“Entrepreneurs are told that the best way to start a company is to solve a problem they understand. It makes sense that those problems range from how to get booze delivered 24/7 to how to build a cloud-based enterprise human resources system – the tangible problems in the life and work of a 25- or 30-year-old.”
If it really is the case that entrepreneurs only look at problems they understand or are on their immediate event horizon then clearly we need more entrepreneurs of my age group (let’s just say 45+). We are the people either with elderly parents, like my mum, who are facing the very real problems of old age and poor health and who themselves will very soon be facing the same issues.
“For healthcare in particular, the timing for a game changer couldn’t be better. The industry is coping with upheaval triggered by varied economic, societal and industry influences. Empowered consumers living in an increasingly digital world are demanding more from an industry that is facing growing regulation, soaring costs and a shortage of skilled resources.”
At SXSW, which is running this week in Austin, Texas IBM is providing an exclusive look at its cognitive technology called Watson and showcasing a number of inspiring as well as entertaining applications of this technology. In particular on Tuesday 15th March there is a session called Ageing Populations & The Internet of Caring Things where you can take a look at accessible technology and how it will create a positive impact on an aging person’s quality of life.
Also at SXSW this year President Obama gave a keynote interview where he called for action in the tech world, especially for applications to improve government IT. The President urged the tech industry to solve some of the nation’s biggest problems by working in conjunction with the government. “It’s not enough to focus on the cool, next big thing,” Obama said, “It’s harnessing the cool, next big thing to help people in this country.”
It is my hope that with the vision that people such as Obama have given the experience of getting old will be radically different 10 or 20 years from now and that cognitive and IoT technology will make all of out lives not only longer but more more pleasant.
* Unicorns are referred to companies whose valuation has exceeded $1 billion dollars.
In an earlier post I discussed the UK government report on distributed ledger technology (AKA ‘blockchain‘) and how the government’s Chief Scientific Advisor, Sir Mark Walport, was doing the rounds advocating the use of blockchain for a variety of (government) services.
Blockchain is a shared, trusted, public ledger that everyone can inspect, but which no single user controls. The participants in a blockchain system collectively keep the ledger up to date: it can be amended only according to strict rules and by general agreement. For a quick introduction to blockchain this article in the Economist is a pretty good place to start.
Blockchains are going to be useful wherever there is a need for a trustworthy record, something which is pretty vital for transactions of all sorts whether it be in banking, for legal documents or for registries of things like land or high value art works etc. Startups such as Stampery are looking to use blockchain technology to provide low cost certification services. Blockchain is not just for pure startups however. Twenty-five banks are part of the blockchain company, called R3 CEV, which aims to develop common standards around this technology. R3 CEV’s Head of Technology is Richard Gendal Brown an ex-colleague from IBM.
IBM recently announced that, together with Intel, J.P. Morgan and several large banks, it was joining forces to create the Open Ledger Project with the Linux Foundation, with the goal of re-imagining supply chains, contracts and other ways information about ownership and value are exchanged in a digital economy.
As part of this IBM is creating some great tools, using its Bluemix platform, to get developers up and running on the use of blockchain technology. If you have a Bluemix account you can quickly deploy some applications and study the source code on GitHub to see how to start making use of blockchain APIs.
This service is intended for developers who consider themselves early adopters and want to get involved with IBM’s approach to business networks that maintain, secure and share a replicated ledger using blockchain technology. It shows how you can:
Deploy and invoke simple transactions to test out IBM’s approach to blockchain technology.
Learn and test out IBM’s novel contributions to the blockchain open source community, including the concept of confidential transactions, containerized code execution etc.
It provides some simple demo applications you can quickly deploy into Bluemix to play around with this technology.
This service is not production ready. It is pre-alpha and intended for testing and experimentation only. There are additional security measures that still must be implemented before the service can be used to store any confidential data. That said it’s still a great way to learn about the use and potential for this technology.
This week (Monday 25th) I gave a lecture about IBM’s Watson technology platform to a group of first year students at Warwick Business School. My plan was to write up the transcript of that lecture, with links for references and further study, as a blog post. The following day when I opened up my computer to start writing the post I saw that, by a sad coincidence, Marvin Minsky the American cognitive scientist and co-founder of the Massachusetts Institute of Technology’s AI laboratory had died only the day before my lecture. Here is that blog post, now updated with some references to Minsky and his pioneering work on machine intelligence.
First though, let’s start with Alan Turing, sometimes referred to as “the founder of computer science”, who led the team that developed a programmable machine to break the Nazi’s Enigma code, which was used to encrypt messages sent between units on the battlefield during World War 2. The work of Turing and his team was recently brought to life in the film The Imitation Game starring Benedict Cumberbatch as Turing and Keira Knightley as Joan Clarke, the only female member of the code breaking team.
Sadly, instead of being hailed a hero, Turing was persecuted for his homosexuality and committed suicide in 1954 having undergone a course of hormonal treatment to reduce his libido rather than serve a term in prison. It seems utterly barbaric and unforgivable that such an action could have been brought against someone who did so much to affect the outcome of WWII. It took nearly 60 years for his conviction to be overturned when on 24 December 2013, Queen Elizabeth II signed a pardon for Turing, with immediate effect.
In 1949 Turing became Deputy Director of the Computing Laboratory at Manchester University, working on software for one of the earliest computers. During this time he worked in the emerging field of artificial intelligence and proposed an experiment which became known as the Turing test having observed that: “a computer would deserve to be called intelligent if it could deceive a human into believing that it was human.”
The idea of the test was that a computer could be said to “think” if a human interrogator could not tell it apart, through conversation, from a human being.
Turing’s test was supposedly ‘passed’ in June 2014 when a computer called Eugene fooled several of its interrogators that it was a 13 year old boy. There has been much discussion since as to whether this was a valid run of the test and that the so called “supercomputer,” was nothing but a chatbot or a script made to mimic human conversation. In other words Eugene could in no way considered to be intelligent. Certainly not in the sense that Professor Marvin Minsky would have defined intelligence at any rate.
In the early 1970s Minsky, working with the computer scientist and educator Seymour Papert, wrote a book called The Society of Mind, which combined both of their insights from the fields of child psychology and artificial intelligence.
Minsky and Papert believed that there was no real difference between humans and machines. Humans, they maintained, are actually machines of a kind whose brains are made up of many semiautonomous but unintelligent “agents.” Their theory revolutionized thinking about how the brain works and how people learn.
Despite the more widespread accessibility to apparently intelligent machines with programs like Apple Siri Minsky maintained that there had been “very little growth in artificial intelligence” in the past decade, saying that current work had been “mostly attempting to improve systems that aren’t very good and haven’t improved much in two decades”.
Minsky also thought that large technology companies should not get involved the field of AI saying: “we have to get rid of the big companies and go back to giving support to individuals who have new ideas because attempting to commercialise existing things hasn’t worked very well,”
Whilst much of the early work researching AI certainly came out of organisations like Minsky’s AI lab at MIT it seems slightly disingenuous to believe that commercialistion of AI, as being carried out by companies like Google, Facebook and IBM, is not going to generate new ideas. The drive for commercialisation (and profit), just like war in Turing’s time, is after all one of the ways, at least in the capitalist world, that innovation is created.
Which brings me nicely to Watson.
IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data. It is named after Thomas J. Watson, the first CEO of IBM, who led the company from 1914 – 1956.
IBM Watson was originally built to compete on the US television program Jeopardy. On 14th February 2011 IBM entered Watson onto a special 3 day version of the program where the computer was pitted against two of the show’s all-time champions. Watson won by a significant margin. So what is the significance of a machine winning a game show and why is this a “game changing” event in more than the literal sense of the term?
Today we’re in the midst of an information revolution. Not only is the volume of data and information we’re producing dramatically outpacing our ability to make use of it but the sources and types of data that inform the work we do and the decisions we make are broader and more diverse than ever before. Although businesses are implementing more and more data driven projects using advanced analytics tools they’re still only reaching 12% of the data they have, leaving 88% of it to go to waste. That’s because this 88% of data is “invisible” to computers. It’s the type of data that is encoded in language and unstructured information, in the form of text, that is books, emails, journals, blogs, articles, tweets, as well as images, sound and video. If we are to avoid such a “data waste” we need better ways to make use of that data and generate “new knowledge” around it. We need, in other words, to be able to discover new connections, patterns, and insights in order to draw new conclusions and make decisions with more confidence and speed than ever before.
For several decades we’ve been digitizing the world; building networks to connect the world around us. Today those networks connect not just traditional structured data sources but also unstructured data from social networks and increasingly Internet of Things (IoT) data from sensors and other intelligent devices.
These additional sources of data mean that we’ve reached an inflection point in which the sheer volume of information generated is so vast; we no longer have the ability to use it productively. The purpose of cognitive systems like IBM Watson is to process the vast amounts of information that is stored in both structured and unstructured formats to help turn it into useful knowledge.
There are three capabilities that differentiate cognitive systems from traditional programmed computing systems.
Understanding: Cognitive systems understand like humans do, whether that’s through natural language or the written word; vocal or visual.
Reasoning: They can not only understand information but also the underlying ideas and concepts. This reasoning ability can become more advanced over time. It’s the difference between the reasoning strategies we used as children to solve mathematical problems, and then the strategies we developed when we got into advanced math like geometry, algebra and calculus.
Learning: They never stop learning. As a technology, this means the system actually gets more valuable with time. They develop “expertise”. Think about what it means to be an expert- – it’s not about executing a mathematical model. We don’t consider our doctors to be experts in their fields because they answer every question correctly. We expect them to be able to reason and be transparent about their reasoning, and expose the rationale for why they came to a conclusion.
The idea of cognitive systems like IBM Watson is not to pit man against machine but rather to have both reasoning together. Humans and machines have unique characteristics and we should not be looking for one to supplant the other but for them to complement each other. Working together with systems like IBM Watson, we can achieve the kinds of outcomes that would never have been possible otherwise:
IBM is making the capabilities of Watson available as a set of cognitive building blocks delivered as APIs on its cloud-based, open platform Bluemix. This means you can build cognition into your digital applications, products, and operations, using any one or combination of a number of available APIs. Each API is capable of performing a different task, and in combination, they can be adapted to solve any number of business problems or create deeply engaging experiences.
So what Watson APIs are available? Currently there are around forty which you can find here together with documentation and demos. Four examples of the Watson APIs you will find at this link are:
Understand someones personality from what they have written.
It’s never been easier to get started with AI by using these cognitive building blocks. I wonder what Turing would have made of this technology and how soon someone will be able to pin together current and future cognitive building blocks to really pass Turing’s famous test?
You can always tell when a technology has reached a certain level of maturity when it gets its own slot on the BBC Radio 4 news program ‘Today‘ which runs here in the UK every weekday morning from 6am – 9am.
Yesterday (Tuesday 19th January) morning saw the UK government’s Chief Scientific Advisor, Sir Mark Walport, talking about blockchain (AKA distributed ledger) and advocating its use for a variety of (government) services. The interview was to publicise a new government report on distributed ledger technology (the Blackett review) which you can find here.
The report has a number of recommendations including the creation of a distributed ledger demonstrator and calls for collaboration between industry, academia and government around standards, security and governance of distributed ledgers.
As you would expect there are a number of startups as well as established companies working on applications of distributed ledger technology including R3CEV whose head of technology is Richard Gendal Brown, an ex-colleague of mine from IBM. Richard tweets on all things blockchain here and has a great blog on the subject here. If you want to understand blockchain you could take a look at Richard’s writings on the topic here. If you want an extremely interesting weekend read on the current state of bitcoin and blockchain technology this is a great article.
IBM, recognising the importance of this technology and the impact it could have on society, is throwing its weight behind the Linux Foundations project that looks to advance this technology following the open source model.
From a software architecture perspective I think this topic is going to be huge and is ripe for some first mover advantage. Those architects who can steal a lead on not only understanding but explaining this technology are going to be in high demand and if you can help with applying the technology in new and innovative ways you are definitely going to be a rockstar!
So, the future has finally arrived and today is ‘Back to the Future Day‘. Just in case you have missed any of the newspaper, internet and television reports that have been ‘flying’ around this week, today is the day that Marty McFly and Doc Brown travel to in the 1980s movie Back To The Future IIas dialled into the very high-tech (I love the Dymo labels) console of the modified (i.e. to make it fly) Delorean DMC-12 motor car. As you can see the official time we can expect Marty and Doc Brown to arrive is (or was) 04:29 (presumably that’s Pacific Time).
Depending on when you read this therefore you might still get a chance to watch one of the numerous Marty McFly countdown clocks hitting zero.
Most of the articles have focussed on how its creators did or didn’t get the technology right. Whilst things like electric cars, wearable tech, drones and smart glasses have come to fruition what’s more interesting is what the film completely missed i.e. the Internet, smartphones and all the gadgets which we now take for granted thanks to a further 30 years (i.e. since 1985, when the first film came out) of Moore’s Law.
Coincidentally one day before ‘Back to the Future’ day I gave a talk to a group of university students which was focussed on how technology has changed in the last 30 years due to the effects of Moore’s Law. It’s hard to believe that back in 1985, when the first Back to the Future film was released, a gigabyte of hard disk storage cost $71,000 and a megabyte of RAM cost $880. Today those costs are 5 cents and a lot less than 1 cent respectively. This is why it’s now possible for all of us to be walking around carrying smart devices which have more compute power and storage than even the largest and fastest super computers of a decade or so ago.
It’s also why the statement made by Jim Deters, founder of the education community Galvanise, is so true, namely that today:
“Two guys in a Starbucks can have access to the same computing power as a Fortune 500 company.”
Today anyone with a laptop, a good internet connection and the right tools can set themselves up to disrupt whole industries that once seemed secure and impeneterable to newcomers. These are the disruptors who are building new business models that are driving new revenue streams and providing great, differentiated client experiences (I’m talking the likes of Uber, Netflix and further back Amazon and Google). People use the term ‘digital Darwinism’, meaning the phenomenon of technology and society evolving faster than an organization can adapt, to try and describe what is happening here. As Charles Darwin said:
“It’s not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change.”
Interestingly IBM is working with Galvanise in San Francisco at its Bluemix Garage where it brings together entrepreneurs and start ups, as well as established enterprises, to work with new platform as a service (PaaS) tools like IBM Bluemix, Cloudant and Watson to help them create and build new and disruptive applications. IBM also recently announced its Bluemix Garage Method which aims to combine industry best practices on Design Thinking, Lean Startup, Agile Development, DevOps, and Cloud to build and deliver innovative and disruptive solutions.
There are a number of Bluemix Garages opening around the world (currently they are in London, Toronto, Nice and Melbourne) as well as local pop-up garages. If you can’t get to a garage and want to have a play with Bluemix yourself you can sign up for a free registration here.
It’s not clear how long Moore’s Law has left to run and whether non-silicon based technologies, that overcome some of the laws of physics that are threatening the ongoing exponential growth of transistors in chips, will ever be viable. It’s also not clear how relevant Moore’s Law actually is in the age of Cloud computing. One thing that is certain however is that we already have access to enough technology and tools that mean we are only limited by our ideas and imaginations in creating new and disruptive business models.
Now, where did I leave my hoverboard so I can get off to my next meeting.
“The only way to learn a new programming language is by writing programs in it. The first program to write is the same for all languages: Print the words ‘hello, world’.”
So started the introduction to the book The C Programming Language by Brian Kernighan and Dennis Ritchie back in 1978. Since then many a programmer learning a new language has heeded those words of wisdom by trying to write their first program to put up those immortal words on their computer screens. Even the Whitehouse is now in on the game.
You can find a list of how to write “hello, world” in pretty much any language you have ever heard of (as well as some you probably haven’t) here. The idea of writing such a simple program is not so much that it will teach you anything about the language syntax but it will teach you how to get to grips with the environment that the code (whether compiled or interpreted) runs in. Back in 1978 when C ran under Unix on hardware like Digital Equipment Corporation’s PDP-11 the environment was a relatively simple affair consisting of a processor, some storage and rudimentary cathode ray terminal (CRT). Then the ‘environment’ amounted to locating the compiler, making sure the right library was provided to the program and figuring out the options to run the compiler and the binary files output. Today things are a bit more complicated which is why the basic premise of getting the most simple program possible (i.e. writing ‘hello, world’ to a screen) is still very relevant as a way of learning the environment.
All of this is by way of an introduction to how to get ‘hello, world’ to work in the IBM Bluemix Platform as a Service (PaaS) environment. In case you haven’t heard, IBM Bluemix is an open source platform based on Cloud Foundry that provides developers with a complete set of DevOps tools to develop, deploy and maintain web and mobile applications in the cloud with minimal hassle. Bluemix-hosted applications have access to the capabilities of the underlying cloud infrastructure to support the type of non-functional requirements (performance, availability, security etc) that are needed to support enterprise applications. Bluemix also provides a rich set of services to extend your applications with capabilities like analytics, social, internet of things and even IBM Watson cognitive services. The Bluemix platform frees developers and organizations from worrying about infrastructure-related plumbing details and focus on what matters to their organizations – business scenarios that drive better value for their customers.
Step 1: Sign Up for a Free Bluemix Trial
You can sign up for a free Bluemix trial (and get an an IBM ID if you don’t have one) here. You’ll need to do this before you do anything else. The remainder of this tutorial assumes you have Bluemix running and you are logged into your account.
Step 2: Download the Cloud Foundry Command Line Interface
You can write code and get it up and running in numerous ways in Bluemix including within Bluemix itself, using Eclipse tools or with the Cloud Foundry command line interface (CLI). As this example uses the latter you’ll need to ensure you have the CLI downloaded on your computer. To do that follow the instructions here.
Step 3: Download the Example Code
You can download the code for this example from my GitHub here. Thanks to Carl Osipov over at Clouds with Carl for this code. Once you have downloaded the zip file unpack it into a convenient folder. You will see there are three files (plus a readme).
package.json – which tells Bluemix it needs a Node.js runtime.
manifest.yml – this file is used when you deploy your code to Bluemix using the command line interface. It contains the values that you would otherwise have to type on the command line when you ‘push’ your code to Bluemix. I suggest you edit this and change the ‘host’ parameter to something unique to you (e.g. change my name to yours).
Step 4: Deploy and Run the Code
Because all your code and the instructions for deploying it are contained in the three files just downloaded deploying into Bluemix is simplicity itself. Do the following:
Open a command a Command Prompt window.
Change to the directory that you unpacked the source code into by typing: cd your_directory.
Login to Bluemix with your IBM ID credentials: cf login -u user-id -o password -s dev. Here dev is the Bluemix space you want to use (‘dev’ by default).
Deploy your app to Bluemix by typing: cf push.
That’s it! It will take a while to upload, install and start the code and you will receive a notification when it’s done. Once you get that response back on the command line you can switch to your Bluemix console and should see this.
To show the program is working you can either click on the ‘Open URL’ widget (the square with the right pointing arrow in the hello-world-node-js application) or type the URL: ‘hello-world-node-js-your-name.mybluemix.net’ into a browser window (your-name is whatever you set ‘host’ to in the manifest file). The words ‘hello, world’ will magically appear in the browser. Congratulations you have written and deployed your first Bluemix app. Pour yourself a fresh cup of coffee and bask in your new found glory.
If you live in the UK and would like to learn more about the IBM Bluemix innovation platform then sign up for this free event in London at the Rainmaking Loft on Thursday 25th June 2015 here.