Tech Guides

The following guide serves as an excellent learning roadmap for functional programming. It can be used to track our level of knowledge regarding functional programming. This guide was developed for the Fantasyland institute of learning for the LambdaConf conference. It was designed for statically-typed functional programming languages that implement category theory. This post is extracted from the book Hands-On Functional Programming with TypeScript by Remo H. Jansen. In this book, you will understand the pros, cons, and core principles of functional programming in TypeScript. This roadmap talks about five levels of difficulty: Beginner, Advanced Beginner, Intermediate, Proficient, and Expert. Languages such as Haskell support category theory natively, but, we can take advantage of category theory in TypeScript by implementing it or using some third-party libraries. Not all the items in the list are 100% applicable to TypeScript due to language differences, but most of them are 100% applicable. Beginner To reach the beginner level, you will need to master the following concepts and skills: CONCEPTS SKILLS Immutable data Second-order functions Constructing and destructuring Function composition First-class functions and lambdas Use second-order functions (map, filter, fold) on immutable data structures Destructure values to access their components Use data types to represent optionality Read basic type signatures Pass lambdas to second-order functions Advanced beginner To reach the advanced beginner level, you will need to master the following concepts and skills: CONCEPTS SKILLS Algebraic data types Pattern matching Parametric polymorphism General recursion Type classes, instances, and laws Lower-order abstractions (equal, semigroup, monoid, and so on) Referential transparency and totality Higher-order functions Partial application, currying, and point-free style Solve problems without nulls, exceptions, or type casts Process and transform recursive data structures using recursion Able to use functional programming in the small Write basic monadic code for a concrete monad Create type class instances for custom data types Model a business domain with abstract data types (ADTs) Write functions that take and return functions Reliably identify and isolate pure code from an impure code Avoid introducing unnecessary lambdas and named parameters Intermediate To reach the intermediate level, you will need to master the following concepts and skills: CONCEPTS SKILLS Generalized algebraic data type Higher-kinded types Rank-N types Folds and unfolds Higher-order abstractions (category, functor, monad) Basic optics Implement efficient persistent data structures Existential types Embedded DSLs using combinators Able to implement large functional programming applications Test code using generators and properties Write imperative code in a purely functional way through monads Use popular purely functional libraries to solve business problems Separate decision from effects Write a simple custom lawful monad Write production medium-sized projects Use lenses and prisms to manipulate data Simplify types by hiding irrelevant data with existential Proficient To reach the proficient level, you will need to master the following concepts and skills: CONCEPTS SKILLS Codata (Co)recursion schemes Advanced optics Dual abstractions (comonad) Monad transformers Free monads and extensible effects Functional architecture Advanced functors (exponential, profunctors, contravariant) Embedded domain-specific languages (DSLs) using generalized algebraic datatypes (GADTs) Advanced monads (continuation, logic) Type families, functional dependencies (FDs) Design a minimally powerful monad transformer stack Write concurrent and streaming programs Use purely functional mocking in tests. Use type classes to modularly model different effects Recognize type patterns and abstract over them Use functional libraries in novel ways Use optics to manipulate state Write custom lawful monad transformers Use free monads/extensible effects to separate concerns Encode invariants at the type level. Effectively use FDs/type families to create safer code Expert To reach the expert level, you will need to master the following concepts and skills: CONCEPTS SKILLS High performance Kind polymorphism Generic programming Type-level programming Dependent-types, singleton types Category theory Graph reduction Higher-order abstract syntax Compiler design for functional languages Profunctor optics Design a generic, lawful library with broad appeal Prove properties manually using equational reasoning Design and implement a new functional programming language Create novel abstractions with laws Write distributed systems with certain guarantees Use proof systems to formally prove properties of code Create libraries that do not permit invalid states. Use dependent typing to prove more properties at compile time Understand deep relationships between different concepts Profile, debug, and optimize purely functional code with minimal sacrifices Summary This guide should be a good resource to guide you in your future functional-programming learning efforts. Read more on this in our book Hands-On Functional Programming with TypeScript. What makes functional programming a viable choice for artificial intelligence projects? Why functional programming in Python matters: Interview with best selling author, Steven Lott Introducing Coconut for making functional programming in Python simpler

To answer this question, let’s start by defining what exactly JavaScript fatigue is. JavaScript fatigue is best described as viewing the onslaught of new JavaScript tools, frameworks or packages as a relentless stream of shaggy dog stories instead of an endless stream of creativity and enhanced productivity. I must admit, I myself have a serious case of JavaScript fatigue. Anyone who is plugged into the tech world knows that JavaScript has been having a moment since the release of Node.js in 2009. Obviously, JavaScript was not new in 2009. Its monopoly on web scripting had already made it an old hand in the development world, but with the advent of Node.js, JavaScript began to creep out of web browsers into desktop applications and mobile apps. Pretty soon there was the MEAN stack, a web app architecture that allows for the developer to run a web app end-to-end with only JavaScript, and tools like PhoneGap allowing developers to create mobile apps with good old fashioned HTML, CSS and, you guessed it, JavaScript. I think JavaScript fatigue asks the question, should we really be excited about the emergence of ‘new’ tech based on or built for a scripting language that has been in use for almost 30 years? How did JavaScript fatigue happen? Before I answer the title question, let’s discuss how this happened. Obviously, just the creation/emergence of Node.js cannot be considered the complete explanation of JavaScript fatigue. But, when you consider that JavaScript happens to be a relatively ‘easy’ language, and the language that many people start their development journeys with, a new platform that extended the functionality of such a language (Node.js) easily became a catalyst for the JavaScript wave that has been rolling for the last few years. So, the really simple answer is that JavaScript is easy, so a bunch of people are using it. But who cares? Why is it that a bunch of people using a language that most of us already know is a bad thing? To me that sounds a lot like a good thing. The reason this is problematic actually has nothing to do with JavaScript. There is a difference between using a common language because it is productively advantageous and using a common language because of laziness. Many developers are guilty of the latter. And when a developer is lazy about one thing, they’re probably lazy about all the other things as well. Is it fair to blame JavaScript? So why are there so many lazily created frameworks, APIs, web apps and desktop applications created in JavaScript? Is it really fair to blame the language? No it is not fair. People are not really fed up with JavaScript, they’re fed up with lazy developers, and that is nothing new. Outside of literal laziness in the writing of JS code, there is a laziness based around picking the tools to solve problems. I’ve heard it said that web development or any development for that matter is really not about development tools or process, it's about the results. Regular people don’t care what technologies Amazon uses on their website, while everybody cares about using Amazon to buy things or stream videos. There has been a lot of use of JavaScript for the sake of using JavaScript. This is probably the most specific reason people are talking about JavaScript fatigue. When hammering a nail into a board, a carpenter doesn’t choose a screwdriver because the screwdriver is the newest tool in their toolbox, they choose a hammer, because it's the right tool. Sure, you could use the handle of the screwdriver to bang in that nail, and it would basically work, and then you would get to use your new tool. This is clearly a stupid way to operate. Unfortunately, many of the choices made in the development world today are centered on finding the newest JavaScript tool to solve a problem instead of finding the best tool to solve a problem. If developers eat up new tools like candy, other developers are going to keep creating them. This is the downward spiral we find ourselves in. Using technology to solve problems So, why is everyone talking about JavaScript fatigue? Because it is a real problem, and it's getting real annoying. As has been the case before, many developers have become Narcissus, admiring their code in the reflective pool of the Internet, until they turn to stone. Let’s keep an eye on the prize: using technology to solve problems. If JavaScript is used in this way, nobody would have any qualms with the current JavaScript renaissance. It's when we start developing for the sake of developing that things get a little weird.

Unity has undoubtedly been one of the leaders when it comes to developing cross-platform products - going from strengths to strengths in developing visually stimulating 2D as well as 3D games and simulations.With Artificial Intelligence revolutionizing the way games are being developed these days, Unity have identified the power of Machine Learning and introduced Unity Machine Learning Agents. With this, they plan on empowering the game developers and researchers in their quest to develop intelligent games, robotics and simulations. What are Unity Machine Learning Agents? Traditionally, game developers have been hard-coding the behaviour of the game agents. Although effective, this is a tedious task and it also limits the intelligence of the agents. Simply put, the agents are not smart enough. To overcome this obstacle, Unity have simplified the training process for the game developers and researchers by introducing Unity Machine Learning Agents (ML-Agents, in short). Through just a simple Python API, the game agents can be now trained to use deep reinforcement learning, an advanced form of machine learning, to learn from their actions and modify their behaviour accordingly. These agents can then be used to dynamically modify the difficulty of the game. How do they work? As mentioned earlier, the Unity ML-Agents are designed to work based on the concept of deep reinforcement learning, a branch of machine learning where the agents are trained to learn from their own actions. Here is a simple flowchart to demonstrate how reinforcement learning works: The reinforcement learning training cycle The learning environment to be configured for the ML-agents consists of 3 primary objects: Agent: Every agent has a unique set of states, observations and actions within the environment, and is assigned rewards for particular events. Brain: A brain decides what action any agent is supposed to take in a particular scenario. Think of it as a regular human brain, which basically controls the bodily functions. Academy: This object contains all the brains within the environment To train the agents, a variety of scenarios are made possible by varying the connection of different components (explained above) of the environment. Some are single agents, some simultaneous single agents, and others could be co-operative and competitive multi-agents and more. You can read more about these possibilities on the official Unity blog. Apart from the way these agents are trained, Unity are also adding some cool new features in these ML-agents. Some of these are: Monitoring the agents’ decision-making to make it more accurate Incorporating curriculum learning, by which the complexity of the tasks can eventually be increased to aid more effective learning Imitation learning is a newly-introduced feature wherein the agents simply mimic the actions we want them to perform, rather than they learning on their own. What next for Unity Machine Learning Agents? Unity recently announced the release of v0.3 beta SDK of the ML-agents, and have been making significant progress in this domain to develop smarter, more intelligent game agents which can be used with the Unity game engine. Still very much in the research phase, these agents can also be used as an example by academic researchers to study the complex behaviour of trained models in different environments and scenarios where the variables associated with the in-game physics and visual appearance can be altered. Going forward, these agents can also be used by enterprises for large scale simulations, in robotics and also in the development of autonomous vehicles. These are interesting times for game developers, and Unity in particular, in their quest for developing smarter, cutting-edge games. Inclusion of machine learning in their game development strategy is a terrific move, although it will take some time for this to be perfected and incorporated seamlessly. Nonetheless, all the research and innovation being put into this direction certainly seems well worth it!

Facial recognition technology is not new. In fact, it has been around for more than a decade. However, with the recent rise in artificial intelligence and deep learning, facial technology has achieved new heights. In addition to facial detection, modern day facial recognition technology also recognizes faces with high accuracy and in unfavorable conditions. It can also recognize expressions and analyze faces to generate insights about an individual. Deep learning has enabled a power-packed face recognition system, all geared up to achieve widespread adoption. How has deep learning modernised facial recognition Traditional facial recognition algorithms would recognize images and people using distinct facial features (placement of eye, eye color, nose shape etc.) However, they failed in correct identification in cases of different lighting or slight change in the appearance ( beard growth, aging, or pose). In order to develop facial recognition techniques for a dynamic and ever-changing face, deep learning is proving to be a game changer. Deep Neural nets go beyond the approach of manual extraction. These AI based Neural Networks rely on image pixels to analyze features of a particular face. So they scan faces irrespective of the lighting, ageing, pose, or emotions. Deep learning algorithms remember each time they recognize or fail to recognize a problem. Thus, avoiding repeat mistakes and getting better at each attempt. Deep learning algorithms can also be helpful in converting 2D images to 3D. Facial recognition in practice: Facial Recognition Technology in Multimedia Deep learning enabled facial recognition technologies can be used to track audience reaction and measure different levels of emotions. Essentially it can predict how a member of the audience will react to the remaining film. Not only this, it also helps determine what percentage of users will be interested in a particular movie genre. For example, Microsoft’s Azure Emotion,  an emotion API detects emotions by analysing the facial expressions on an image or video content over time. Caltech and Disney have collaborated to develop a neural network which can track facial expressions. Their deep learning based Factorised Variational Autoencoders (FVAEs) analyze facial expressions of audience for about 10 minutes and then predict how their reaction will be for the rest of the film. These techniques help in estimating whether the viewers are giving the expected reactions at the right place. For example, the viewer is not expected to yawn on a comical scene. With this, Disney can also predict the earning potential of a particular movie. It can generate insights that may help producers create compelling movie trailers to maximize the number of footfalls. Smart TVs are also equipped with sophisticated cameras and deep learning algos for facial recognition ability. They can recognize the face of the person watching and automatically show channels and web applications programmed as their favorites. The British broadcasting corporation uses the facial recognition technology, built by CrowdEmotion. By tracking faces of almost 4,500 audience members watching show trailers, they gauge exact customer emotions about a particular programme. This in turn helps them generate insights to showcase successful commercials. Biometrics in Smartphones A large number of smartphones nowadays are instilled with biometric capabilities. Facial recognition in smartphones are not only used as a means of unlocking and authorizing, but also for making secure transactions and payments. In present times, there has been a rise in chips with built-in deep learning ability. These chips are embedded into smartphones. By having a neural net embedded inside the device, crucial face biometric data never leaves the device or sent to the cloud. This in turn improves privacy and reduces latency. Some of the real-world examples include Intel’s Nervana Neural Network Processor, Google’s TPU, Microsoft’s FPGA, and Nvidia’s Tesla V100. Deep learning models, embedded in a smartphone, can construct a mathematical model of the face which is then stored in the database. Using this mathematical face model, smartphones can easily recognize users even as their face ages or when it is obstructed by wearable accessories. Apple has recently launched the iPhone X facial recognition system termed as FaceID. It maps thousands of points on a user’s face using a projector and an infrared camera (which can operate under varied lighting conditions). This map is then passed to a bionic chip embedded in the smart phone. The chip has a neural network which constructs a mathematical model of the user’s face, used for biometric face verification and recognition. Windows Hello is also a facial recognition technology to unlock Windows smart devices equipped with infrared cameras. Qualcomm, a mobile technology organization, is working on a new depth-perception technology. It will include an image signal processor and high-resolution 3D depth-sensing cameras for facial recognition. Face recognition for Travel Facial recognition technologies can smoothen the departure process for a customer by eliminating the need for a boarding pass. A traveller is scanned by cameras installed at various check points, so they don’t have to produce a boarding pass at every step. Emirates is collaborating with Dubai Customs, Police and Airports to use a facial recognition technology solution integrated with the UAE Wallet app. The project is known as Together Initiative, it allows travellers to register and store their biometric facial data at several kiosks placed at the check-in area. This facility helps passengers to avoid presenting their physical documents at every touchpoint. Face recognition can also be used for determining illegal immigration. The technology compares the photos of passengers taken immediately before boarding, with the photos provided in their visa application. Biometric Exit, is an initiative by US government, which uses facial recognition to identify individuals leaving the country. Facial recognition technology can also be used at train stations to reduce the waiting time for  buying a train ticket or going through other security barriers. Bristol Robotics Laboratory has developed a software which uses infrared cameras to identify passengers as they walk onto the train platform. They do not need to carry tickets. Retail and shopping In the area of retail, smart facial recognition technologies can be helpful in fast checkout by keeping a track of each customer as they shop across a store. This smart technology, can also use machine learning and analytics to find trends in the shopper’s purchasing behavior over time and devise personalized recommendations. Facial video analytics and deep learning algorithms can also identify loyal and VIP shoppers from the moving crowd, giving them a privileged VIP experience. Thus, enabling them with more reasons to come back and make repeat purchases. Facial biometrics can also accumulate rich statistics about demographics(age, gender, shopping history) of an individual. Analyzing these statistics can generate insights, which helps organizations develop their products and marketing strategies. FindFace is one such platform that uses sophisticated deep learning technologies to generate meaningful data about the shopper. Its e-facial recognition system can verify faces with almost 99% accuracy. It can also help route the shopper data to a salesperson’s notice for personalized assistance. Facial recognition technology can also be used to make secure payment transactions simply by analysing a person’s face. AliBaba has set up a Smile to Pay face recognition system in KFC's. This system allows customers to make secure payments by merely scanning their face. Facial recognition has emerged as a hot topic of interest and is poised to grow. On the flip side, organizations deploying such technology should incorporate privacy policies as a standard measure. Data collected from such facial recognition software can also be used wrongly for targeting customers with ads, or for other illegal purposes. They should implement a methodical and systematic approach for using facial recognition for the benefit of their customers. This will not only help businesses generate a new source of revenue, but will also usher in a new era of judicial automation.  

At Packt, we are always on the lookout for innovative startups that are not only pushing the boundaries of technology but also making a positive impact on our world. In this edition of our "Eye on New Tech Startups" series, we are excited to introduce you to Meaningful Planet. This forward-thinking company is harnessing the power of technology to drive environmental change and sustainability.  Read on to discover how Meaningful Planet is balancing technological advancement with environmental responsibility and learn how you can be part of their mission to create a better future. Introducing Meaningful Planet - Taking a Tech Approach to Restoring Nature and our Planet In a world where technological advancements are rapidly transforming every aspect of our lives, it's crucial to consider the environmental impact of these innovations. At Meaningful Planet, we believe that tech can be a force for good, and we are committed to using it to drive meaningful environmental change.  As a tech-for-good company, we are on a mission to empower individuals and businesses to make a tangible positive impact on the environment through everyday essential services. You can find out more about our launch product and the plans we offer here, a UK mobile SIM only network. Our Mission At the heart of Meaningful Planet lies a simple yet powerful mission: to make the world a better place by integrating environmental consciousness into our everyday actions.  We achieve this by providing mobile, broadband, and energy services that are not only top-tier in quality but also contribute directly to environmental restoration projects. By dedicating 10% of all revenue to these projects, we ensure that our business growth translates into real-world positive impact. Tech for Good Our commitment to the environment extends beyond our core services. We understand that the technology we rely on can have a significant environmental footprint, particularly with the increasing demands of cloud computing. Therefore, we strive to balance this impact by leveraging technology to address broader climate issues. Our Tech Stack At Meaningful Planet, we build our systems using a robust tech stack that includes Ruby on Rails and JavaScript. This combination allows us to develop scalable, efficient, and high-performing applications that meet the needs of our users while maintaining a focus on sustainability. Ruby on Rails: Known for its simplicity and efficiency, Ruby on Rails helps us quickly develop and iterate our backend systems, reducing development time and energy consumption. JavaScript: Our front-end applications are powered by JavaScript, enabling us to create dynamic, responsive user experiences that engage and inspire our customers to be part of the environmental solution. Balancing Tech Advancements with Environmental Responsibility While the advancement of technology brings countless benefits, it also comes with challenges, particularly in terms of energy consumption and emissions. Cloud computing, for example, is essential for modern applications but can contribute to increased carbon emissions. At Meaningful Planet, we address this challenge in several ways: Efficient Coding Practices: We prioritize writing efficient, optimized code to minimize resource usage and reduce the carbon footprint of our applications. Green Hosting Solutions: We partner with eco-friendly hosting providers that utilize renewable energy sources and implement sustainable practices in their data centers. Continuous Monitoring and Optimization: Our team continuously monitors our systems to identify areas for improvement, ensuring that our infrastructure remains as energy-efficient as possible. Utilizing Tech to Address the Climate Crisis Beyond mitigating our own impact, we harness the power of technology to drive broader environmental change. Through our platform, we enable users to track their environmental contributions, engage with sustainability initiatives, and make informed choices that benefit the planet. Environmental Impact Tracking: Our system provides users with real-time data on how their choices contribute to environmental projects, fostering a sense of connection and accountability. Community Engagement: We leverage tech to build a community of like-minded individuals and businesses who are passionate about making a difference. Our platform facilitates collaboration, knowledge sharing, and collective action. Join Us in Making a Meaningful Difference We invite the Packt community and developers worldwide to join us on this journey. By integrating environmental consciousness into our tech practices and services, we can collectively drive significant positive change. Whether you're looking to switch to a more sustainable service provider or interested in collaborating on innovative solutions, Meaningful Planet offers a unique opportunity to be part of a mission that matters.  You can find out more about the meaningful projects we support to achieve our impact here. Let's harness the power of technology to create a sustainable future. Together, we can make a meaningful difference. In Closing We hope you found this spotlight on Meaningful Planet as inspiring as we did. As developers and tech enthusiasts, we have the power to influence the future of technology and its impact on our world. Meaningful Planet is a shining example of how technology can be harnessed for the greater good.  Stay tuned for more insights and stories about groundbreaking startups in our "Eye on New Tech Startups" series. Let's continue to support and innovate towards a more sustainable and ethical tech industry.

Software systems are vulnerable. That's down to a range of things, from the constant changes our software systems undergo, to the extent of the opportunities for criminals to take advantage of the gaps and vulnerabilities within these systems. Fortunately, penetration testers - or ethical hackers - are a vital line of defence. Yes, you need to properly understand the nature of cyber security threats before you take steps to tackle them, but penetration testing tools are the next step towards securing your software. There's famous saying from Stephane Nappo that sums up cyber security today: It takes 20 years to build a reputation and few minutes of cyber-incident to ruin it. So, make sure you have the right people with the right penetration testing tools to protect not only your software but your reputation too.  The most popular penetration testing tools Kali Linux Kali linux is a Linux distro designed for digital forensics and penetration testing. The predecessor of BackTrack, it has grown in adoption to become one of the most widely used penetration testing tools. Kali Linux is  based on debian - most of its packages are imported from Debian repositories. Kali includes more than 500 preinstalled penetration testing programs that makes it possible to exploit wired, wireless, and ARM devices. The recent release of Kali Linux 2018.1 supports Cloud penetration testing. Kali has collaborated with some of the planet's leading cloud platforms such as AWS and Azure, helping to change the way we approach cloud security. Metasploit Metasploit is another popular penetration testing framework. It was created in 2003 using Perl and was acquired by Rapid7 in 2009 by which time it was completely rewritten in Ruby. It is a collaboration of the open source community and Rapid 7 with the outcome being the Metasploit Project well known for its anti-forensic and evasion tools. Metasploit is a concept of ‘exploit’ which is a code that is capable of surpassing any security measures entering vulnerable systems. Once through the security firewalls, it runs as a ‘payload’, a code that performs operations on a target machine, as a result creating the ideal framework for penetration testing. Wireshark WireShark is one of the world’s primary network protocol analyzers also popular as a packet analyzer. It was initially released as Ethereal back in 1998 and due to some trademark issues was renamed to WireShark in 2006. Users usually use WireShark for network analysis, troubleshooting, and software and communication protocol development. Wireshark basically functions in the second to seventh layer of network protocols, and the analysis made is presented in a human readable form. Security Operations Center analysts and network forensics investigators use this protocol analysis technique to analyze the amount of bits and bytes flowing through a network. The easy to use functionalities and the fact that it is open source makes Wireshark one of the most popular packet analyzers for security professionals and network administrators who want to quickly earn money as freelancers. Burp Suite Threats to web applications have grown in recent years. Ransomware and cryptojacking have become increased techniques used by cybercriminals to attack users in the browser. Burp or Burp Suite is one widely used graphical tool for testing web application security. Since it's about application security there are two versions to this tool: a paid version that include all the functionalities and the free version that comes with few important functionalities. This tool comes preinstalled with basic functionalities that will help you with web application security checks. If you are looking at getting into web penetration testing this should definitely be your first choice as it works with Linux, Mac and Windows as well. Nmap Nmap also known as Network Mapper is a security scanner. As the name suggests it builds a map of the network to discover hosts and services on a computer network. Nmap follows a set of protocols to function where it sends a crafted packet to the target host and then analyses the responses. It was initially released in 1997 and since then it has provided a variety of features to detect vulnerabilities and network glitches. The major reason why one should opt for Nmap is that it is capable of adapting to network conditions like network delay and network congestion during a scan. To keep your environment protected from security threats you should take necessary measures. There are n number of penetration testing tools out there with exceptional capabilities. The most important thing would be to choose the necessary tool based on your environment’s requirement. You can pick and choose from the above mentioned tools as they are shortlisted taking into consideration the fact that they are effective, well supported and easy to understand and most importantly they are open-source. Learn some of the most important penetration testing tools in cyber security Kali Linux - An Ethical Hacker's Cookbook, Metasploit Penetration Testing Cookbook - Third Edition Network Analysis using Wireshark 2 Cookbook - Second Edition For a complete list of books and videos on this topic, check out our penetration testing products.

"There are two kinds of companies, those that work to try to charge more and those that work to charge less. We will be the second."-- Jeff Bezos, CEO Amazon When Jeff Bezos launched Amazon.com in 1994, it was an online bookselling site. This was during a time when bookstores such as Barnes & Noble, Waldenbooks and Crown Books were the leading front runners in the bookstore industry in the American shopping malls. Today, Amazon’s name has become almost synonymous with online retail for most people and has now spread its wings to cloud computing, electronics, tech gadgets and the entertainment world. With market capitalization worth $897.47B as of August 3rd 2018, it’s hard to believe that there was a time when Amazon sold only books. Amazon is constantly pushing to innovate and as new inventions come to shape, there are “patents” made that helps the company have a competitive advantage over technologies and products in order to attract more customers. [box type="shadow" align="" class="" width=""]According to United States Patent and Trademark Office (USPTO), Patent is an exclusive right to invention and “the right to exclude others from making, using, offering for sale, or selling the invention in the United States or “importing” the invention into the United States”.[/box] As of March 20, 2018, Amazon owned 7,717 US patents filed under two business entities, Amazon Technologies, Inc. (7,679), and Amazon.com, Inc (38). Looking at the chart below, you can tell that Amazon Technologies, Inc., was one among the top 15 companies in terms of number of patents granted in 2017. Top 15 companies, by number of patents granted by USPTO, 2017 Amazon competes closely with the world’s leading tech giants in terms of patenting technologies. The below table only considers US patents. Here, Amazon holds only few US patents than IBM, Microsoft, Google, and Apple.  Number of US Patents Containing Emerging-Technology Keywords in Patent Description Some successfully patented Amazon innovations in 2018 There are thousands of inventions that Amazon is tied up with and for which they have filed for patents. These include employee surveillance AR goggles, a real-time accent translator, robotic arms tossing warehouse items,  one-click buying, drones,etc. Let’s have a look at these remarkable innovations by Amazon. AR goggles for improving human-driven fulfillment (or is it to track employees?) Date of Patent: August 2, 2018 Filed: March 20, 2017 Assignee: Amazon Technologies, Inc.   AR Goggles                                                          Features: Amazon has recently patented a pair of augmented reality goggles that could be used to keep track of its employees.The patent is titled “Augmented Reality User interface facilitating fulfillment.” As per the patent application, the application is a wearable computing device such as augmented reality glasses that are worn on user’s head. The user interface is rendered upon one or more lenses of the augmented reality glasses and it helps to show the workers where to place objects in Amazon's fulfillment centers. There’s also a feature in the AR glasses which provides workers with turn-by-turn directions to the destination within the fulfillment centre. This helps them easily locate the destination as all the related information gets rendered on the lenses.    AR Goggles  steps The patent has received criticism over concerns that this application might hamper the privacy of employees within the warehouses, tracking employees’ every single move. However, Amazon has defended the application by saying that it has got nothing to do with “employee surveillance”. As this is a patent, there’s no guarantee if it will actually hit the market. Robotic arms that toss warehouse items Date of Patent: July 17, 2018 Filed: September 29, 2015 Assignee: Amazon Technologies, Inc. Features: Amazon won a patent titled “Robotic tossing of items in inventory system” last month. As per the patent application, “Robotic arms or manipulators can be used to toss inventory items within an inventory system. Tossing strategies for the robotic arms may include information about how a grasped item is to be moved and released by a robotic arm to achieve a trajectory for moving the item to a receiving location”.  Robotic Arms Utilizing a robotic arm to toss an item to a receiving location can help improve throughput through the inventory system. This is possible as the robotic arms will help with reducing the amount of time that may otherwise be spent on placing a grasped item directly onto a surface for receiving the item. “The tossing strategy may be based at least in part upon a database containing information about the item, characteristics of the item, and/or similar items, such as information indicating tossing strategies that have been successful or unsuccessful for such items in the past,” the patent reads.  Robotic Arms Steps Amazon’s aim with this is to eliminate the challenges faced by modern inventory systems like supply chain distribution centers, airport luggage systems, etc, while responding to requests for inventory items. The patent received criticism over the concern that one of the examples in the application was a dwarf figurine and could possibly mock people of short stature. But, according to Amazon, “The intention was simply to illustrate a robotic arm moving products, and it should not be taken out of context.” Real-time accent translator Date of Patent: June 21, 2018 Filed: December 21, 2016 Assignee: Amazon Technologies, Inc. Features: Amazon won a patent for an audio system application, titled “Accent translation” back in June this year, which will help with translating the accent of the speaker to the listener’s accent. The aim with this app is to get rid of the possible communication barriers which may arise due to different accents as they can be difficult to understand at times. Accent translation system The accent translation system collects a number of audio samples from different sources such as phone call, television, movies, broadcasts, etc. Each audio sample will have its association with at least one of the accent sample sets present in its database.  For instance, german accent will be associated with the german accent sample set.   Accent translation system steps In a two-party dialog, acquired audio is analyzed and if it associates with one among a wide range of saved accents then the audio from both the sides is outputted based on the accent of the opposite party. The possibilities with this application are endless. One major use case is the customer care industry where people have to constantly talk to different people with different accents. Drone that uses Human gestures and voice commands Date of Patent: March 20, 2018 Filed: July 18, 2016 Assignee: Amazon Technologies, Inc. Features: Amazon patented for a drone, titled “Human interaction with unmanned aerial vehicles”, earlier this year, that would use human gestures and voice commands for package delivery. Amazon Drone makes use of propulsion technology which will help with managing the speed, trajectory, and direction of the drone.   Drones As per the patent application, “an unmanned aerial vehicle is provided which includes propulsion device, sensor device and a management system. The management system is configured to receive human gestures via the sensor device and in response, instruct the propulsion device to affect and adjustment to the behavior of the unnamed aerial vehicle. Human gestures include-- visible gestures, audible gestures, and other gestures capable of recognition by the unmanned vehicle”. Working structure of drones The concept for drones started when Amazon CEO, Jeff Bezos, promised, back in 2013, that the company aims to make 30-minute deliveries, of packages up to 2.25 kgs or 5 pounds. Amazon’s patents are a clear indication of its efforts and determination for inventing cutting-edge technologies for optimizing its operations so that it can pass on the benefits to its customers in the form of competitively priced product offerings. As Amazon has been putting its focus on machine learning, the drones and robotic arms will make the day-to-day tasks of the facility workers easier and more efficient. In fact, Amazon has stepped up its game big time and is incorporating Augmented reality, with its AR glasses to further scale efficiencies. The real-time accent translators help eliminate the communication barriers, making Amazon cover a wide range of areas and perhaps provide a seamless customer care experience in the coming days. Amazon Echo vs Google Home: Next-gen IoT war Amazon is selling facial recognition technology to police  

On February 28th, 2017, the Raspberry Pi Foundation announced the latest product in the Raspberry Pi series – the Raspberry Pi Zero W. The new product adds wireless connectivity to the Raspberry Pi Zero and is being retailed for just $10. This is great news for enthusiasts and hobbyists all around the world.  Wait, wait, Raspberry Pi? Raspberry Pi Zero? Wireless? What are we talking about? Okay, so, to understand the idea behind Raspberry Pi Zero W and the benefits it brings, we need to back up a bit and talk about the Raspberry Pi series of products and its history. The Raspberry Pi's history The Raspberry Pi is a computer that's the size of a credit card and was made available to the public for the low price of $35. And yes, despite the size and the price of the product, it's a full-fledged computer capable of running an operating system like Linux and Android, though Windows is a bit too heavy for it to run. It came with 2 USB ports and a HDMI port so you can plug your keyboard, mouse, and monitor into it and treat it just like your everyday computer.  The first generation of the Raspberry Pi was released in February 2012 and was an instant hit among the DIY and hobbyist crowd. The small-sized and low-priced computer proved to be perfect to power up their DIY projects. By the time this post was written, 10 million Raspberry Pi computers have been sold and countless numbers of projects using the miniature computer have been made. It has been used in projects including: home arcade boxes, automated pet feeders, media centers, security cameras, and many, many others.  The second generation of the Raspberry Pi was launched in February 2015. The computer now offered a higher-clocked, quad-core processor with 1 GB of RAM and was still being sold at $35. Then, a year later in February 2016, the Raspberry Pi 3 was launched. While the price remained the same, this latest generation of the computer boasted higher performance as well as wireless connectivity via WiFi and Bluetooth.  What's better than a $35 computer?  The Raspberry Pi has come a long way but, with all of that said, do you know what's better than a $35 computer? A $5 computer that’s even smaller, which is exactly what was launched in November 2015: the Raspberry Pi Zero. Despite its price, this new computer is actually faster than the original Raspberry Pi and, by using micro USB and mini HDMI instead of the normal-sized port, the Raspberry Pi Zero managed to shrink down to just half the size of a credit card.  Unfortunately, using micro USB and mini HDMI ports leads to another set of problems. Most people need additional dongles or converters to connect to those ports, and those accessories can be as expensive as the computer itself. For example: a micro-USB to Ethernet connector will cost $5, a micro-USB to USB connector will cost $4, and a micro-USB WiFi adapter will cost $10.  Welcome the Raspberry Pi Zero W  Needing additional dongles and accessories that cost as much as the computer itself pretty much undermines the point of a cheap computer. So to mitigate that the Raspberry Pi Zero W, a Raspberry Pi Zero with integrated WiFi and Bluetooth connectivity, was introduced in February 2017 at the price of $10. Here are the hardware specifications ofthe Raspberry Pi Zero W: Broadcom BCM2835 single-core CPU @1GHz 512MB LPDDR2 SDRAM Micro USB data port Micro USB power port Mini HDMI port with 1080p60 video output Micro SD card slot HAT-compatible 40-pin header Composite video and reset headers CSI camera connector 802.11n wireless LAN Bluetooth 4.0  Its dimensions are 65mm x 30mm x 5mm (for comparison, the size of a Raspberry Pi 3 is 85mm x 56mm x 17mm).  There are several things to note about the hardware. One of them is that the 40-pin GPIO connector is not soldered out of the box; you have to solder it yourself. These unsoldered connectors are what allow the computer to be so slim and will be pretty useful to people who don't need a GPIO connection.  Another thing to note is that the wireless chip is the same wireless chip found in the Raspberry Pi 3, so they should behave and perform pretty similarly. And because the rest of the hardware is basically the same as the ones found in the Raspberry Pi Zero, you can think of the RaspberryPi Zero W as a fusion between both series.  Is the wireless connectivity worth the added cost? You may wonder if the wireless connectivity is worth the additional $5. Well, it really depends on your use case. For example, in my home everything is already wireless and I don't have any LAN cables that I can plug in to connect to the Internet, so wireless connectivity is a really big deal for me.  And really, there are a lot of projects and places where having wireless connectivity could help a lot. Imagine if you want to setup a camera in front of your home that would send an email to you every time it spots a particular type of car. Without a WiFi connection, you would have to pull your Ethernet cable all the way out there to have an Internet connection. And it's not just the Internet to consider – having Bluetooth connectivity is a really practical way to connect to other devices, like your phone for instance.  All in all, the Raspberry Pi Zero W is a great addition to the Raspberry Pi line of computers. It's affordable, it's highly capable, and with the addition of wireless connectivity it has become practical to use too. So go get your hands on one and start your own project today.  About the author Raka Mahesa is a game developer at Chocoarts: chocoarts.com, who is interested in digital technology in general. In his spare time, he likes to work on his own projects, with Corridoom VR being his latest released game. Raka also regularly tweets as @legacy99. 

Machine learning can be done using many languages, with Python and R being the most popular. But one language has been overlooked for some time—Julia. Why isn’t Julia machine learning a thing? Julia isn't an obvious choice for machine learning simply because it's a new language that has only recently hit version 1.0. While Python is well-established, with a large community and many libraries, Julia simply doesn't have the community to shout about it. And that's a shame. Right now Julia is used in various fields. From optimizing milk production in dairy farms to parallel supercomputing for astronomy, Julia has a wide range of applications. A common theme here is that these actions all require numerical, scientific, and sometimes parallel computation. Julia is well-suited to the sort of tasks where intensive computation is essential. Viral Shah, CEO of Julia Computing said to Forbes “Amazon, Apple, Disney, Facebook, Ford, Google, Grindr, IBM, Microsoft, NASA, Oracle and Uber are other Julia users, partners and organizations hiring Julia programmers.” Clearly, Julia is powering the analytical nous of some of the most high profile organizations on the planet. Perhaps it just needs more cheerleading to go truly mainstream. Why Julia is a great language for machine learning Julia was originally designed for high-performance numerical analysis. This means that everything that has gone into its design is built for the very things you need to do to build effective machine learning systems. Speed and functionality Julia combines the functionality from various popular languages like Python, R, Matlab, SAS and Stata with the speed of C++ and Java. A lot of the standard LaTeX symbols can be used in Julia, with the syntax usually being the same as LaTeX. This mathematical syntax makes it easy for implementing mathematical formulae in code and make Julia machine learning possible. It also has in-built support for parallelism which allows utilization of multiple cores at once making it fast at computations. Julia’s loops and functions features are pretty fast, fast enough that you would probably notice significant performance differences against other languages. The performance can be almost comparable to C with very little code actually used. With packages like ArrayFire, generic code can be run on GPUs. In Julia, the multiple dispatch feature is very useful for defining number and array-like datatypes. Matrices, data tables work with good compatibility and performance. Julia has automatic garbage collection, a collection of libraries for mathematical calculations, linear algebra, random number generation, and regular expression matching. Libraries and scalability Julia machine learning can be done with powerful tools like MLBase.jl, Flux.jl, Knet.jl, that can be used for machine learning and artificial intelligence systems. It also has a scikit-learn implementation called ScikitLearn.jl. Although ScikitLearn.jl is not an official port, it is a useful additional tool for building machine learning systems with Julia. As if all those weren’t enough, Julia also has TensorFlow.jl and MXNet.jl. So, if you already have experience with these tools, in other implementations, the transition is a little easier than learning everything from scratch. Julia is also incredibly scalable. It can be deployed on large clusters quickly, which is vital if you’re working with big data across a distributed system. Should you consider Julia machine learning? Because it’s fast and possesses a great range of features, Julia could potentially overtake both Python and R to be the choice of language for machine learning in the future. Okay, maybe we shouldn’t get ahead of ourselves. But with Julia reaching the 1.0 milestone, and the language rising on the TIOBE index, you certainly shouldn’t rule out Julia when it comes to machine learning. Julia is also available to use in the popular tool Jupyter Notebook, paving a path for wider adoption. A note of caution, however, is important. Rather than simply dropping everything for Julia, it will be worth monitoring the growth of the language. Over the next 12 to 24 months we’ll likely see new projects and libraries, and the Julia machine learning community expanding. If you start hearing more noise about the language, it becomes a much safer option to invest your time and energy in learning it. If you are just starting off with machine learning, then you should stick to other popular languages. An experienced engineer, however, who already has a good grip on other languages shouldn’t be scared of experimenting with Julia - it gives you another option, and might just help you to uncover new ways of working and solving problems. Julia 1.0 has just been released What makes functional programming a viable choice for artificial intelligence projects? Best Machine Learning Datasets for beginners

For quite sometime now virtual machines (VMs) have gained a lot of traction. The major reason for this trend was IT industries were totally convinced about the fact that instead of having a huge room filled with servers, it is better to deploy all your workload on a single piece of hardware. There is no doubt that virtual machines have succeeded as they save a lot of cost and work pretty well making failovers easier. In a similar sense when containers were introduced they received a lot attention and have recently gained even more popularity amongst IT organisations. Well, there are a set of considerable reasons for this buzz; they are highly scalable, easy to use, portable, have faster execution and are mainly cost effective. Containers also subside management headaches as they share a common operating system. With this kind of flexibility it is quite easier to fix bugs, place update patches and make other alterations. All-in-all containers are lightweight and more portable than virtual machines. If all of this is true, are virtual machines going extinct? Well, for this answer you will have to deep dive into the complexities of both worlds. How Virtual Machines work? A virtual machine is an individual operating system installed on your usual operating system. The entire implementation is done by software emulation and hardware virtualization. Usually multiple virtual machines are used on servers where the physical machine remains the same but each virtual environment runs a completely separate service. Consider a Ubuntu server as a VM and use it to install all or any service you need. Now, if your deployment needs a set of software to handle web applications you provide all the necessary services to your application. Suddenly, there is a requirement for an additional service where your situation gets tighter, as all your resources are preoccupied. All you need to do is, install the new service on the guest virtual machine and you are all set to relax. Advantages of using virtual machines Multiple OS environments can run simultaneously on the same physical machine Easy to maintain, highly available, convenient recovery, and application provisioning Virtual machines tend to be more secure than containers Operating system flexibility on VMs is better than that of containers Disadvantages of using virtual machines Simultaneously running virtual machines may introduce an unstable performance, depending on the workload on the system by other running virtual machines Hardware accessibility becomes quite difficult when it comes to virtual machines Virtual machines are heavier in size taking up several gigabytes How Containers work? You can consider containers as lightweight, executable packages that provide everything an application needs to run and function as desired. A container usually sits on top of a physical server and its host OS allowing applications to run reliably in different environments by subtracting the operating system and physical infrastructure. So where VMs depend totally on hardware we have a new popular kid in town that requires significantly lesser hardware and does the task with ease and efficiency. Suppose you want to deploy multiple web servers faster, containers make it easier. The reason for this is, as you are deploying single services the containers require lesser hardware compared to virtual machines. The benefit of using containers does not end here. Docker, a popular container solution, creates a cluster of docker engines in such a way that they are managed as a single virtual system. So if you’re looking at deploying apps with scale, and lesser failovers your first preference should be containers. Advantages of using Containers You can any day add more computing workload on the same server as containers consume less resources Servers can load more containers than virtual machines as they are usually in megabytes Containers makes it easier to allocate resources to processes which helps running your applications in different environments Containers are cost effective solutions that help in decreasing both operating and development cost. Bug tracking and testing is easier in containers as there isn’t any difference in running your application locally, or on test servers, or in production Development, testing, and deployment time decreases with containers Disadvantages of using Containers Since containers share the kernel and other components of host operating system it become more vulnerable and can impact security of other containers as well Lack of operating system flexibility. Everytime you want to run a container on a different operating system you need to start a new server. Now coming to the original question. Are containers worth it? Will they eliminate virtualization entirely? Well, after reading this article you must have already guessed the clear winner considering the advantages over disadvantages of each platform. So, in virtual machines the hardware is virtualized to run multiple operating system instances. If one needs a complete platform that can provide multiple services then, virtual machines is your answer as it is considered a matured and a secure technology. If you're looking at achieving high scalability, agility, speed, lightweight, and portability, all this comes under just one hood, containers. With this standardised unit of software, one can stay ahead of the competition. If you still have concerns over security and how a vulnerable kernel can jeopardize the cluster than you need, DevSecOps is your knight in shining armor. The whole idea of DevSecOps is to bring operations and development together with security functions. In a nutshell, everyone involved in a software development life cycle is responsible for security. Kubernetes Containerd 1.1 Integration is now generally available Top 7 DevOps tools in 2018 What’s new in Docker Enterprise Edition 2.0?

Are you confused about which front-end tools you should learn, and which ones you should work with? Do you want to know other web developers are using and what they think is important when it comes to front-end frameworks and libraries?? Fear not! We have the answer to all these questions. In our annual skill-up survey, we spoke to over 8,000 developers and tech pros and asked them about the front-end tools, libraries, and frameworks they regularly use. Of course, choosing the perfect front-end technology depends on your skills, and your area of interest. However, seeing the umpteen number of front-end frameworks available nowadays, we have narrowed it down to just a few. jQuery [box type="shadow" align="alignleft" class="" width=""]Creators: John Resig. Released: 2006 Current version: 3.3.1 Popularity: 49,100 stars on GitHub[/box] jQuery came out to be the undisputed champion of this showdown, with over 70% of respondents choosing it as their go-to front-end library. jQuery is a fast, lightweight and concise Javascript library mainly used for HTML document traversing, event handling, animating, and Ajax interactions for rapid web development. It’s a cross-platform JavaScript library meaning it simplifies Javascript development by reducing coding time. Although Javascript has a large no. of libraries jQuery stands out because of its unlimited tutorials, no cross-platform/browser issues, great user interface, a large no, of plugins, and light, fast, and quick to learn nature. Essentially, jQuery is most suited for applications which need rapid development. Get started with jQuery with the following resources: Getting Started with jQuery 3 [Video] Beginning JavaScript and jQuery [Video] Bootstrap [box type="shadow" align="alignleft" class="" width=""]Creators: Mark Otto and Jacob Thornton. Released: 2011 Current version: 4.1.1 Popularity: 125,108 stars on GitHub[/box] Over 65% of developers choose Bootstrap as their favorite framework to use. And why not, considering Bootstrap is everywhere. Bootstrap is an open source toolkit for developing with HTML, CSS, and JS. The huge popularity of Bootstrap is mainly because of its simplistic use, great community, and a very large number of articles and tutorials, third-party plugins and extensions, theme builders, etc. Our top picks for learning Bootstrap: Bootstrap 4 Site Blueprints Learning Web Development with React and Bootstrap Bootstrap 4 Cookbook Npm (Node package manager) [box type="shadow" align="alignleft" class="" width=""] Creators: Isaac Z. Schlueter. Released: 2011 Current version: 6.1.0 Popularity:16,597 stars on GitHub[/box] NPM is the package manager for node. With NPM, developers can install various modules for web development, share and borrow packages, and manage private development. It consists of three distinct components: Website: The website can be used to discover packages, set up profiles, and manage other aspects of the npm experience. Command Line Interface (CLI): The CLI runs from a terminal. Developers can interact with npm through CLI. Registry: The registry is a large public database of JavaScript software and the meta-information surrounding it. It contains over 600,000 packages (building blocks of code). AngularJS [box type="shadow" align="alignleft" class="" width=""] Creators: Google. Released: 2016 Current version: 6.0.0 Popularity:58,576 stars on GitHub[/box] If you’re looking to build a dynamic and robust Single page applications, AngularJS is the framework you need. Angular is a core part of the MEAN stack (MongoDB, ExpressJS, AngularJS, and Node.js)  So now you can use javascript for both your site’s frontend and backend. Angular is highly modular, making it great for dividing up large-scale work with a team and also makes testing and debugging easy. It pairs with AJAX for amazing speed and can handle heavy user interaction via forms. The functionality-first approach makes Angular more focused on features, making the developers’ jobs easier. Moreover, it has excellent tools and support from the Google community. Start learning Angular with these books. Learning Angular - Second Edition Essential Angular 4 Angular By Example - Third Edition Webpack [box type="shadow" align="alignleft" class="" width=""] Creators: Tobias Koppers, Sean Larkin, Johannes Ewald, Juho Vepsäläinen, and Kees Kluskens. Released: 2012 Current version: 4.8.3 Popularity:41,366 stars on GitHub[/box] Webpack is a module bundler for modern JavaScript applications. Webpack is a tool that’s been around for a number of years but has recently seen its popularity grow. And this is the reason developers awarded it the no. 5 spot on our list. Webpack, is, quite simply brings all the assets you need in front-end development – like JavaScript, fonts, and images, in one place. This is particularly useful if you’re developing complicated front ends. You can go through Deploying Web Applications with Webpack to get up-and-running with Webpack. The other frontend tools which were among the top picks by developers included: Sass: Sass is a web design framework, a CSS preprocessor, which adds special features such as variables, nested rules, and mixins into regular CSS. React: React is one of the most popular javascript libraries for building UI interfaces. It provides speed, simplicity, and scalability for creating single-page applications and mobile applications. Gulp: An open-source javascript toolkit, Gulp is mainly used for automation tasks such as bundling and minifying libraries and stylesheets, quickly running unit tests, running code analysis, etc. Vue: Incrementally adoptable, Vue is a fast-growing Javascript framework. It is much simpler than Angular, both in terms of API, so in terms of design. It is a presentation layer, instead of a full-scale framework. Thus you can easily combine Vue with other libraries. The entire skill up survey report can be downloaded from the Packt website, which details what developers think and feel about the changing tech landscape. Developers think managers don’t know enough about technology. And that’s hurting business. Don’t call us ninjas or rockstars, say, developers 96% of developers believe developing soft skills is important

If you have been paying attention to the world of web development lately, it can seem a little chaotic. There are brand new frameworks and libraries that come out each and every day. These frameworks are sometimes related to previous ones that recently came out, or they are attempting to develop something entirely new. As new technologies emerge, they change things that could have been standard for a long time. With these changes happening fairly often, it can be beneficial or frustrating, depending on the situation. Let's take a look at why the creation of new technologies in web development could be a benefit for some developers, or a negative for others. Why change and novelty in web development is awesome Let's first take a look at how the rapid changes in web development can be a wonderful thing for some developers. New tools and techniques to learn With new tech constantly emerging, you will always have something new to learn as a developer. This keeps the field interesting (at least for me and other developers I know that actually like the field). It allows you to continuously add to your skillset as well. You will constantly be challenged with the newer frameworks when learning them, which will help you learn future technologies faster. Having this skill of being a constant learner is crucial in a field that is always improving. Competition When there are a high number of frameworks that do similar things, the best ones will be the ones that are used by the majority of people. For instance, there are tons of front-end frameworks like React and Angular, but React and Angular are the ones that survive simply because of their popularity and ease of use. This is similar to how capitalism works: Only the best will survive. This creates a culture of innovation in the web development community and causes even more tech to be developed, but at a higher quality. Even better products A large amount of technology being released in a short period of time allows for developers to develop creative and stunning web pages using various combinations of technologies working together. If websites are stunning and easy to use, businesses are more likely to get customers to use their products. If customers are more likely to use products, that probably means they are spending money and therefore growing the economy. Who does not love awesome products anyway? Workflows become more efficient and agile When better web development tools are created, it becomes easier for other web developers out there to create their own web apps. Usually newer technologies present a brand new way of accomplishing something that happened to previously be more difficult. With this increased ability it allows you to build on top of the shoulder of giants, allowing new developers to create something that previously was too difficult or time consuming. Why change and novelty in web development is a pain Now let's take a look at how the ever-changing state of web development can be a bad thing for web developers. New tools require more learning time With each new technology, the user must learn exactly how it works and how it can even benefit their company or project. There is some time in the beginning that must be spent on actually figuring out how to get the new technology to work. Depending on the documentation, this can sometimes be easier than others, but that extra time can definitely hurt if you are attempting to hit a hard deadline. Identifying risk v. reward can be a challenge With attempting something new, there is always a risk involved. It can turn out that this framework will take up a large portion of your time to implement, and it may only give you a minor performance increase, or minor reduction in development time. You must make this tradeoff yourself. Sometimes it is worth it, other times it definitely is not. Support lifespans are getting shorter for many tools Something that is not popular or widely used will tend to lose support. You may have been an early adopter, when you thought that this technology would be great. Just because the technology was supported today, does not mean it will be supported in the future, when you plan on using it. Support can sometimes make or break the usage of an application and it can sometimes be safer to go with a more stable framework. In my opinion, an ever-changing web development landscape is a good thing, and you just need to keep up. But I've attempted to give you both sides of the coin in order for you to make a decision on your own. Antonio Cucciniello is a Software Engineer with a background in C, C++ and JavaScript (Node.js) from New Jersey. His most recent project called Edit Docs is an Amazon Echo skill that allows users to edit Google Drive files using your voice. He loves building cool things with software, reading books on self-help and improvement, finance, and entrepreneurship. Follow him on twitter @antocucciniello, and follow him on GitHub.

Security often gets forgotten as the technological world quenches its thirst for innovation. Many of the most exciting developments – for both consumers and businesses – have developed way ahead of security concerns. Trends such as IoT and wearables, for example, both present challenges for cybersecurity strategists and white hat hackers. As we bridge the gap between hardware and software new challenges emerge. Arguably, as software becomes more and more embedded in everyday life – both literally and metaphorically – the lines between cybersecurity and traditional security and surveillance become much more blurred too. Recent high profile cases have put cybersecurity on the agenda, even if it remains a subject that’s little understood. The celebrity iCloud hack was a reminder that even the largest systems, built by tech giants and used by hundreds of millions of people, can be attacked. In the UK in 2015 broadband company Talk Talk underwent a massive security attack with 157,000 customers’ data put at risk – the fact that 2 teenage boys were later arrested for the incident only serves to underline that cybersecurity is a strange place. On the one hand demonstrating the disturbing power of a couple of kids with exceptional hacking skills; on the other, the vulnerability of entire systems and infrastructures. Managing security in a world built on Open Source software and cloud The paradoxes of cybersecurity mirror those of the wider software world. On the one hand we’ve never felt more powerful - when it comes to technology, change feels inevitable and exhilarating. Yet the more powerful we feel – as both consumers and programmers, the more we remember the tools that build awesome products, that help us manage huge projects and infrastructures, are largely built and maintained by communities. It is precisely because those tools of apparent progress seem democratic and open that they can be undone, used against the very things they build. While Open Source may be one reason that cybersecurity has become more challenging and complex, it’s worth also thinking about how we use software. The role of cloud in managing software and delivering services in particular has had an impact. Our devices – our digital lives – are no longer intermittently connected to the ‘world wide web’ (a phrase that sounds somewhat dated today) but rather continuously in communication with ‘the cloud’ – information and services are always available. Frictionless user experiences are great for users, but they’re also pretty useful for cybercriminals too. We feel powerless when it comes to security. Yet this lack of power is paradoxically wedded to our contemporary experience of complete control and connectivity and the ability to craft and cultivate our lives online exactly as we want. We want software that is built around our lifestyles with minimum friction, yet that often comes at a price. Consider, for example, what security looked like 10 years ago. The most essential step to being ‘safe’ online was to make sure your firewall was active and your antivirus was up to date. Today that can be difficult, as multiple devices access a range of networks even in the space of a single day (from airport Wi-Fi to mobile data). It’s hard to keep up. The issue isn’t just one for everyday consumers; it’s also a problem for cybersecurity teams developing the products we need. Security is all about stability. This is antithetical to today’s technological ethos. But what can we do to keep systems and infrastructure safe? To keep our information and data secure? How we learned to stop worrying and love hackers But perhaps we’ve found a solution. The emerging phenomenon of the cybersecurity hackathon, in which security experts and friendly hackers are invited to test and expose vulnerabilities, find ways in and around a huge range of software infrastructures. Perhaps the best example of this recently happening was the ‘Hack the Pentagon’ program, the U.S. Government’s ‘bug bounty’, in which more than a thousand security experts (mercenary hackers if you want to be impolite) uncovered hundreds of vulnerabilities in the Pentagon’s software infrastructure. You can find similar events all around the world – organizations whose infrastructure is built on Open Source software are effectively open sourcing their own security capabilities. These sort of events prove that developing security skills (pentesting in particular) can be invaluable, and also a great way to learn more about how software works, and how people have decided to build things. It makes sense. Gone are the days when you could guarantee security with your software package, when you could rely on your contact at Oracle for support. Insofar as most attacks and security risks are always external to a system or an organization, it makes sense to replicate those external threats when trying to identify vulnerabilities and protect yourself. It’s also important to acknowledge that you can’t cover everything internally. It’s easy to be hubristic, but hubris is very often the first sign of weakness. The way forward – UX, IA and security But are hackathons enough? Should cybersecurity in fact be something that we bear greater consideration, as individuals and organizations? Instead of viewing security as a problem to consider at the end of a development process, an irritating inconvenience, by focusing on questions of accessibility and security as design and user experience issues, we can begin to use software in a much smarter and hopefully safer way. For individuals that might mean thinking more carefully about your digital footprint, the data we allow organizations to access. (And yes, maybe we could manage our passwords a little better, but I really didn’t want to include such trite advice…) For businesses and other institutions it may mean aligning cybersecurity with UX and Information Architecture questions. While cybersecurity is definitely a software problem – one which anyone with an inclination towards code should learn more about – it’s also much more than that. It’s a design problem, an issue about where complex software systems interact with the real world in all its complexity and chaos.

More than $350 worth of cryptocurrency guides offered in support of globalgiving.com During Cybersecurity Month, Packt is partnering with Humble Bundle and three other technology publishers – Apress, John Wiley & Sons, No Starch Press - for the Humble Book Bundle: Bitcoin & Cryptocurrency, a starter eBook library of blockchain programming guides offered for as little as $1, with each purchase supporting hurricane disaster relief efforts through the nonprofit, GlobalGiving.org. Packed with over $350 worth of valuable developer information, the bundle offers coding instruction and business insights at every level – from beginner to advanced. Readers can learn how to code with Ethereum while at the same time learning about the latest developments in cryptocurrency and emerging business uses of blockchain programming. As with all Humble Bundles, customers can choose how their purchase dollars are allocated, between the publishers and charity, and can even “gift” a bundle purchase to others as their donation. Donations for as little as $1USD can support hurricane relief. The online magazine retailer, Zinio, will be offering a limited time promotion of some of their best tech magazines as well. You can find the special cryptocurrency package here. "It's very unusual for tech publishers who normally would compete to come together to do good work for a good cause," said Kelley Allen, Director of Books at Humble Bundle. "Humble Books is really pleased to be able to support their efforts by offering this collection of eBooks about such a timely and cutting-edge subject of Cryptocurrency". The package of 15 eBooks includes recent titles Bitcoin for Dummies, The Bitcoin Big Bang, BlockChain Basics, Bitcoin for the Befuddled, Mastering Blockchain, and the eBook bestseller, Introducing Ethereum and Solidity. The promotional bundles are being released globally in English, and are available in PDF, .ePub and .Mobi formats. The offer runs October 9 through October 23, 2017.

Public cloud market seems to be highly dominated by industry giants from the west like Amazon Web Services (AWS) and Microsoft Azure. One of China’s tech giants, Alibaba cloud has entered the public cloud market recently and seems to be catching up pretty quickly with its Software-as-a-Service (SaaS). Infrastructure-as-Service (IaaS), Platform-as-a-Service offerings. According to reports, in December 2017, Alibaba cloud witnessed 56% YoY growth and revenue as good as 12.8 billion USD. It is expected to be better in the Q2 2018 report, where-in the market size will increase to a sizeable amount. Alibaba cloud is already leading China’s cloud market share. It provides around 100 core services, with datacenter spread around 17 regions as a whole. Some of the stunning features of Alibaba cloud include: Elastic Computing ECS services of Alibaba cloud are highly scalable, quick and powerful with high-range Intel CPUs, which brings down the latency to give staggering results. It comes with extra security layer for protecting applications from DDoS and Trojan attacks. The services involved here includes ECS, container services, Autoscaling and so on. Networking Alibaba cloud enables you with hybrid and distributed network, ideal for enterprises which demand high network coverages. This network involves communication between two VPCs and communication between VPCs and IDCs. Security It has a built-in Anti-DDoS management and security assessment services. This definitely reduces the cost of hiring and training quality security engineers to analyze and manage security services and data breaches. Storage and CDN Alibaba cloud’s OSS (Object Storage Service) helps you store, backup, and archive huge amount of data on cloud. This service is absolutely flexible and you only need to pay as per your usage and there are no additional cost involved in it. Analytics It comprises of a wide range of analytics services like business analytics, data processing, stream analytics and so on. Services like Elastic MapReduce, Apache Hadoop and Apache Spark can be run easily on Alibaba cloud for efficient Cloud Analytics. For detailed products and services from Alibaba cloud, refer their official site. AWS and Azure were dominating the public cloud market with an array of services which changed as per the market requirements. Considering the current advancements in the Alibaba cloud and its affordable and highly competitive price range, Alibaba joins the others in the race to dominate the public cloud market. Microsoft Build 2018 Day 1: Azure meets Artificial Intelligence How to create your own AWS CloudTrail Google announce the largest overhaul of their Cloud Speech-to-Text