Blockchain Cryptography Explained to a 9-Year-Old A beginner’s guide to blockchain cryptography C . This should come as no surprise because Bitcoin, the first and most well-known cryptocurrency, took off around 2015. With its meteoric rise, the hype of blockchain technology followed suit. ryptocurrency was the top ranked tech Google search of 2018 Blockchain is the underlying concept and framework that Bitcoin is built upon. As opposed to being dependent on a centralized system, such as using banks, social media companies, and cloud storage, blockchain is a decentralized system that leverages a distributed ledger of record keeping. This 2-minute video produced by the U.S. Treasury gives a great overview of blockchain. In short, the defining features of blockchain that make it an enabler for innovations like cryptocurrency are that it’s , and perhaps most critically, . distributed, decentralized, append-only, consensus-based secured by cryptography If blockchain is cryptocurrency’s underlying skeletal system, then cryptography is its nervous system — it enables information and identity to be securely exchanged between actors throughout a broad network. Cryptography is a vast field of applied mathematics, and in order to distill how it works and connect its importance to blockchain applications within a few paragraphs, I’ll develop an analogy to a classic social game. … Imagine playing the broken telephone game There are a few problems associated with this: The original message has changed The receiver doesn’t know that the message has changed The receiver doesn’t know if the message came from the expected sender In order to solve these issues, blockchain cryptography utilizes a to verify and authenticate the transactions on the network. digital signature (like a seal on the package) The digital signature achieves the following purpose: Ensures that the message has not been tampered with Provides verification that it is the correct person who signed the paper (transaction) No one else has forged or copied the signature Once signed, the signature cannot be repudiated How does the digital signature work? To apply cryptography in the scenario of the telephone game, if person A wants to send message “peas” to person B, he/she would use a private key to “lock” the message. Locking the message here means encrypting the message with asymmetric cryptography. Asymmetric cryptography uses a pair of public and private keys that are arithmetically paired but not identical. The public key can be shared with everyone, but the private key in the pair is kept secret. One key from the pair is used to lock the message, and only the opposite key can be used to unlock the message. Person A’s message then turns into a set of random letters, represented as the secret message in the above illustration because the random letters do not make literal sense. The best analogy I’ve heard describing this process is like turning a chicken into chicken nuggets- it cannot be reversed to figure out the original message. As illustrated, signing the message with the person’s unique private key creates the digital signature. Sending the message When person A is ready to share the message, he/she will send the original message, the public key(the other pair to the private key) and the digital signature as a package to the targeted receiver, person B. Receiving the message When person B receives and opens the package, he/she would take the public message from person A and run it through the same cryptography function to get the secret message. Person B keeps this secret message for verification purpose later. Then person B will take the public key and the digital signature from person A, and run those through a cryptography verification function to get the secret message that’s embedded within the digital signature. Since the private and public keys are arithmetically paired, anyone else’s public key will not be able to unlock the sender’s digital signature. Now, the receiver, B has both secret messages- one encrypted from the cryptography function, and one unlocked from the digital signature. He/she can compare if they are the same. Again, we are not comparing the original message here because the chicken nuggets cannot be transformed back to the chicken. If both secret messages are the same, then the receiver knows the message and the sender are verified as intended. Otherwise, the receiver knows somewhere along that process, something has been changed. I hope my attempt to simplify the blockchain cryptography concept provides a helpful overview for understanding this technology. Thanks for reading!

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There was a time when Procedural languages were the only way to communicate with computers. Procedural languages like C did their jobs quite efficiently, and are still in practice for an array of developments. Then came a time when OOPS made its way in and everything just became for modular. Use C++ or Java to create an object, and use it throughout the project anytime anywhere. However, this was not it. Several other advancements occurred and gave birth to modern programming languages and scripts like Servlets, PHP, C#, ASP, and last but not least — JavaScript. Developed earlier as a scripting language for UI development, JavaScript today is the favorite programming language of many. Over use JavaScript for development. 69.8% of eCommerce developers worldwide JavaScript has the ability to be a front-end scripting language as well as an efficient language for server-side programming too. That’s where Node.js comes into the picture. The abilities of the script make it a perfect language for not just server-side programming, but Node.js for eCommerce development has become a standard to develop massive scales eCommerce sites like Amazon and eBay. This article will explain 5 more reasons that advocate Node.js and explains why a Node.js shopping cart is more reliable than any other solution of any scale. What exactly is Node.js in formal terms? Initially, Node.js was developed as a script to write front end code. However today, it has found its way to a much wider range of applications: It is called as an open-source server environment, and also as an open-source JavaScript run-time environment. Google built is on its Chrome V8 JavaScript engine and made it capable of running on almost every platform: Windows, Linux, UNIX, and Mac OS X. The reason why it’s used for both server-side and front end scripting is that it works on an event-driven non-blocking I/O model, which is capable of writing both on server-side and client-side applications. Moreover, It can also collect data from forms, and perform database operations without changing the syntax. By the time Node.js for eCommerce become prevalent, a number of other programming languages were also getting fame; PHP, Python, Java and many more. PHP is still one of the biggest competitors when it comes to eCommerce development. With successful projects like WordPress, Magento, PrestaShop, Shopify, and OpenCart, PHP proved that it’s a versatile technology. However, there is much about Node.js that makes Node.js shopping carts more simple, versatile, faster and scalable. 1. Node.js is modern with an up-to-date code base Node.js is still a newborn baby if we consider other eCommerce technologies. It means that the code base is modern and according to the contemporary challenges that developer face, and this is indeed true considering the advantages Node.js gives. For example, PHP’s LAMP stack (Linux, Apache, MySQL, and PHP) is more complexed than the MEAN or MERN stacks. The MEAN stack with MongoDB, Express, Angular 2, and Node and MERN stack with MongoDB, Express.js, React, and Node.js, use updated tools, latest security methods, and a huge base library. We will understand this in the subsequent points as well. 2. Node.js uses a faster NoSQL instead of relational database Node.js works both with SQL and NoSQL databases like MongoDB and Couch DB. It executes the queries using graph-based database systems like Neo4j. In addition to that, it’s also compatible with JSON (JavaScript Object Notation), which makes it work even without SQL. You don’t have to switch between different database syntaxes for executing front-end and back-end queries. You can use the same Node.js syntax on the server side and database that you use for client-side scripting. This also makes the NoSQL database non-relational database, which is, of course, faster than other relational databases. 3. Node.js makes system lightning fast with asynchronous compiling Most other programming language today use a relational database, while Node.js can also use NoSQL, which is one of the reasons why Node.js is so fast. However, there is one more reason that makes Node.js even ten-folds faster. Unlike any other programming language, Node.js allows asynchronous code compilation. It means the JavaScript engine compiles your whole program at once and does not wait for the prevailing lines to execute in a defined sequence. Hence, it becomes a non-blocking input/output asynchronous programming language that will generate the output for the individual lines, irrespective of the lines above it executed or not. Below is an example of how to implement the non-blocking I/O architecture in Node.js. You need to use the proxy value to call asynchronous processes: or . Having done that, call ‘ function through the value for taking two asynchronous functions as two different parameters. promise resolve reject then’ promise Now, if it’s meets the condition, the first function will be called. Else, second function will execute in case the condition is failed: reject var myPromise = new Promise(function(resolve, reject) {
  request.get(options, function(err, resp, body) {
    if (err) {
     reject(err);
    } else {
     resolve(JSON.parse(body));
    }
  })
}) 4. Functional inheritance in Node.js is quick and adaptable PHP utilizes prototypal inheritance, which is quick and adaptable. In any case, Node.js additionally gives you a chance to use functional inheritance for a less complex and sans trap framework. For example, PHP’s prototypical inheritance is without a doubt quicker, yet not effectively implementable in real execution. You need to consider both the super constructors and the inherit function to implement it. Doing as such makes your program increasingly inclined to mistakes as you can’t pass a method as a callback parameter without using a wrapper function. In most of the situations, the functional inheritance is easier to implement in Node.js than PHP’s Prototypal inheritance. It is easier in Node.js to bind the methods to their instances. Functional inheritance with node js gives a better performance benchmark and helps you avoid the redundant calling of methods. 5. NPM’s online repository makes it resourceful yet lightweight NPS is a boon to Node.js projects. It gives you instant access to a huge online repository of node.js open-source projects. Moreover, you can use a command-line interface and fetch the required resources right from the repository for tasks such as package installation, update management, version control, and dependency control. With NPM, any Node.js projects become lightweight as you can always re-use code from the repository instead of adding a new one. The best part is you can also download the updates in the source code for the particular code that you are already using from the repository. Final Thoughts I was a fan of PHP as a development technology. I still like it and prefer it for small-scale projects. However, Node.js is way scalable when it comes to building heavy shopping carts with lots of JavaScript. Both MEAN stack and LAMP stack use JavaScript, but MEAN is extremely lightweight and using JavaScript does not slow down the UI. Whereas, LAMP reacts quite unreliable to the JavaScript. As per my familiarity with in the market, this framework is best suited for building the websites of any scale. The kind of flexibility it offers is well optimized for building eCommerce platforms. Node.js shopping cart

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5 reason why Node.js is the best platform to build an eCommerce marketplace

Learn These Guiding Principles To Help You Level-Up Your DevOps Chops A few days ago I was peeling an egg, like an idiot. My girlfriend, Angelly, was like — “why are you peeling the egg like that?” She picked up another hard-boiled egg, smacked it on the cutting board, and then rolled it while pressing down firmly. The egg’s shell was off in about 3 seconds total. Here I was chipping away tiny piece by tiny piece. My point is: Working hard is overrated. The best solutions are the simple, effective solutions. Is it hard to take rust off of a screw? Probably, right? Unless you know that coca-cola removes rust from screws. Now, is it hard? No. All you need to do is drop it in a cup and wait. If you do not know the simple techniques, you cannot use them. You get experimentation instead of implementation. Exploration instead of replication. When a technique in software engineering is used over and over again because it makes the problem you are solving simpler, it becomes a pattern, or “best practice”. Sometimes, they have complex, scary-sounding names — like Command Query Responsibility Segregation (CQRS), or Event Sourcing (ES) — but these patterns exist to solve problems. Specifically, problems found in building sane, distributed systems. When we look at the coding context as a whole there are more universal patterns: “Keep it Simple, Stupid” (KISS), and “Don’t Repeat Yourself” (DRY), for example. I want to talk about these same sorts of patterns and principles as applied to DevOps. DevOps is often represented as the promised land where the birds are singing and the sun is shining. Without the right techniques, though, it will be a pointed spiky hell, like my tiny eggshell shards. On top of the universal patterns like KISS and DRY, there are other “principles” that I’ve found building DevOps Systems that aren’t quite patterns, yet. These principles can help you move from picking away eggshells piece by piece to having them off in no time flat. They are, in no specific order: Don’t repeat others. Empower developers to be productive as possible. There is no production. Put everything in the cluster and backup the whole thing. VPNs are not simple, and there are simple solutions to solve the same things. Codify and Automate Everything. Lesson 1 — Don’t repeat others If you can buy it off the shelf or there is an easy to use open source version of a tool you need, use it. Don’t reinvent the wheel. Buy the wheel. Did you know you can use the same mail server that craigslist uses? Did you know you can use it for free? Need a mail server? Use that! Don’t build it, build around that! I love awesome-self-hosted for finding things, as well as searching for it for use with Helm on their hub. Although Helm is a pretty new tool for finding and sharing software, a v3 website is now up: https://v3.helm.sh/ Helm is really great for getting wheels. Let me take you back to when I started programming. The first “real” language I learned was QBasic in 9th grade. I’d been making websites with HTML and CSS for a few years already. Back then, the internet was new. While people could create packages, they weren’t really sharing them around like we are nowadays. My standard mechanism for storage was a floppy disk — which I wish I could find so I could extract my 11th-grade brick break game I made in Java Swing… When I programmed, I used standard libraries, baby. That was all there really was. That I knew of at least. I was like 13. I’m sure some of you more experienced Java Pros (Hi Vlad and Nick!) were killin’ it. ;) Not today. Hell, today, you can get entire functioning UI components all bundled up nicely and ready to go. You can get a library for dealing elegantly and easily with dates with a single command. Wouldn’t it be great if there were something that allowed you to use and install entire functioning subsystems? Need a database? install database Good news! There is! You can now get entire functioning subsystems all bundled up and ready to go, using Helm. also It’s the same concept we’ve embraced via NPM and Gradle, but the packages it manages are known as Charts. A chart maps containers to run in different ways inside of Kubernetes. Using a chart is kinda like buying a wheel. Except the wheels are containers mapped to run inside of Kubernetes. The cool part about Charts is that you can easily package up your own services to run easily in any Kubernetes cluster, and even share it with others if you’d like. This means that you could describe entire environments in a codified manner: < class= >dependencies:
- name: backup
  repository: < href= >http: version: . - name: monitor
  repository: < href= >http: version: . - name: marketing-site
  repository: < href= >http: version: . - name: denormalizer-service
  repository: < href= >http: version: . - name: mongo
  repository: < href= >https: version: . </code> code "language-markup" a "http://jenkins-x-chartmuseum:8080" //jenkins-x-chartmuseum:8080</a> 0.0 2 a "http://jenkins-x-chartmuseum:8080" //jenkins-x-chartmuseum:8080</a> 0.0 3 a "http://jenkins-x-chartmuseum:8080" //jenkins-x-chartmuseum:8080</a> 1.1 10 a "http://jenkins-x-chartmuseum:8080" //jenkins-x-chartmuseum:8080</a> 1.0 0 a "https://kubernetes-charts.storage.googleapis.com/" //kubernetes-charts.storage.googleapis.com/</a> 1.0 0 Need to update the marketing site to 1.2.0? Just change it, and commit. Which brings us to Lesson Two. Lesson 2 — Empower developers to be as productive as possible So there I was, sitting at my desk, face in my code, tracking down a bug. Users had been complaining about this for weeks and I found some free time and decided to be a hero. Badda bing, badda boom! Found! Fixed! I shouted over my half-height cubicle in the sun-lit room — “I fixed the bug!” Next Tuesday, when the release went out, users were gonna love it! That is, after all of us got together in a room and pushed the boulder of the release up and over the top of another small mountain… err, if we were able to push it over, that was… if nothing goes wrong along the way… Okay, next Tuesday, the release went out — users were gonna love it! if This was my experience deploying when I was a junior software engineer at my first programming job out of college. Things have come a long way since then. Now, I practice trunk based development and deploy many times a day. When I make pull requests, a little bot posts a comment with an ephemeral pull request preview environment after a successful build and passing tests. In today’s environment, you don’t need to shout over the half-height cubicle. The more you can empower engineers to be able to control the parts of the infrastructure that they need, the simpler your job becomes as a DevOps engineer becomes. In lesson one, we saw it was possible to update a production environment by literally changing a number and committing. In the ideal world, where we are also packaging all of our applications into Charts, we are also empowering each engineer on the team to modify how that service runs in production. A chart is literally a mapping of containers to Kubernetes, and for that purpose, it exposes things like resource requirements. Seems how I’m not great at guessing how much memory or CPU settings a new service will need — and I’m guessing you aren’t either — I also like to deploy monitoring and alerting with some rules that will inform me and my team via Slack when those settings need adjusting. This means it will guide you to correct settings once you deploy it. I used to spend hours at a time running Prometheus queries and adjusting, just like I used to spend too much time peeling my eggs. Now, I’ve learned to do it the smart way. I can already hear you saying: “That sounds complicated.” Nope. Just install the chart. Anything that can be automated or simplified should be. For example, what if simply deploying a service with a label that said “expose: true” would map it to a DNS path? This is where operators come in. This is a more advanced Kubernetes helper tool, and something worth understanding, but let’s not get lost in those details right now. And that brings us to Lesson Three. Lesson 3 — There is no production This was one of those epiphany moments for me. It took me looking at things from a new perspective, so try to follow me for a minute. For over a decade, I’d thought there were only a handful of environments. At it’s simplest, there was a Staging Environment and a Production environment. First, you deployed to staging, tested things, and then deployed the next thing, tested it, etc. Once everything was deployed together — “integrated” — then we could continue the process and repeat the process to production. This is how I did things for years and thought nothing of it. It’s the way it was. This is another one of those peeling apart eggshells piece by piece type of moments. A chart is a dependency graph. Use the chart to represent the environment, and then the process of deploying to production is just deploying a single chart! If each team or project or bounded context had its own chart, you could have many production environments that allowed you to group and update sets of services together in a single transaction. So instead of thinking of production as this big, all-encompassing environment, realize there are actually many little productions. Big changes are scary and dangerous. By keeping many small production environments you isolate changes and make the rest of the system more resilient. Also, because everything is just in Charts all the way down, every setting is exposed and tweakable in a uniform manner. Meaning, things like running a less powerful Kafka cluster in staging where it’s not needed is just a simple config change. Lesson 4 — Put everything in the cluster and back up the whole thing Ok, so we can run things easily in production, but what about databases? What about Kafka? Is that safe? Well, if you’ve been paying attention, you’ll remember I mentioned databases as a thing that can be packaged into a chart. In Kubernetes, there is now an API specifically for running stateful applications like databases in highly available setups, called StatefulSets. Pretty much the entire point of Kubernetes is to run containers reliably. Paired with a tool called that backs up an entire Kubernetes cluster, installed via Helm Chart, you can now backup the entire state of a Kubernetes cluster, as well as all of the attached volumes, like the ones created by the StatefulSet, and restore everything with a single command. Backups are also easy to configure on a schedule. Velero With backups and restores just a single command away, coupled with managed Kubernetes, spinning up an entirely new cluster and restoring it to your backed up version can now be done in just two commands. Three if you want a brand new backup first. Instead of thinking of just servers as cattle, entire clusters start to become disposable. Is staging being annoying? Throw it away. Just a simple backup, restore, and DNS change away. Lesson 5 — VPNs are not simple, and there are simple solutions to solve the same things Have you ever using a VPN? enjoyed I mean, honestly. Has ? anyone Google tried. Then, a couple of years back, announced that their company would no longer use them. I guess they didn’t enjoy using them, either. Instead, they rely on trustless networks. Instead of a master key that lets you access all the networks, a key is placed in front of each service via a Single Sign On screen. Need to access the monitoring service? Just log in with your authorized company credentials. A small shift is all that’s required. Instead of using a VPN for authentication, use a proxy. A VPN also places the Kubernetes management URL on a private network where an SSO gateway does not. However, they also provide an alternative authentication mechanism. For Google Cloud and AWS that is IAM authentication, and you can also whitelist IP addresses. If you can maintain a lot less infrastructure with the same benefits, do it. Be like Google: ditch VPNs for trustless networks. Lesson 6 — Codify and Automate Everything Something takes a bit more effort to codify? I don’t care! It will save hours, days, or even weeks of effort later on. Codify it. It’s the only reliably repeatable way to recreate infrastructure. If every setting can be modified by changing and committing something to git, then your technology organization as a whole is essentially declarative. Any dev with git access can easily maintain any system. To do so, use lots of little repositories, then tie them together later. Monorepos lead to people taking shortcuts and a dependency on an artificially important structure. Instead, use lots of little repositories, and tie them together later. My friend Matt and I make a tool for doing this in development, it’s called . Helm is a tool for doing this in production: everything is just a dependency graph! meta Conclusion Don’t peel an egg piece by piece. Smack and roll. Interested in having me automate your DevOps pains away? I’ve got the smack and roll on lock. Check out my website to learn more about me , and my agency, Unbounded to schedule a free 15 minute Discovery call. I’m also available as an advisor on AdvisoryCloud . Just Getting Started with DevOps? Check out My Journey to Acheiving DevOps Bliss, Without Useless AWS Certifications on HackerNoon and find a free 21 day email course at the bottom! Photo Licensed from Adobe Stock Photos, By Africa Studio

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What Does Peeling An Egg Have To Do With DevOps?

Android Unidirectional Data Flow with LiveData Improving Coinverse’s Performance and Structure (UDF) pattern has improved the usability and performance of since the first beta launched in February. Coinverse is the first app creating audiocasts covering technology and news in cryptocurrency. Upgrades using UDF include more efficient newsfeed creation, removal of adjacent native ads, and faster audiocast loading. The Unidirectional Data Flow Coinverse The UDF pattern organizes the app into three main areas, view , and ensuring the app is modularized and reliable. I learned of the UDF pattern from episode 148 of the , with at and . On first listen it was interesting, but a large overhaul. UDF became compelling as I worked on fixing bugs and realized how complex various flows had become. state, events effects Fragmented podcast Evolving Android architectures (Part 1) Kaushik Gopal Instacart Donn Felker The examples of UDF I’ve seen thus far have been with . Rx a powerful and customizable tool for creating streams of data that can be observed in real-time. However, provides the same benefit of observing state changes and on top of that is simple, directly integrates with Android’s , and handles Android’s lifecycle events by default. If unfamiliar, check out Google’s ’s . To try out UDF I refactored the newsfeed flows in Coinverse as we’ll walkthrough below. Rx LiveData architecture components Jose Alcérreca talk on LiveData <a href="https://medium.com/media/8b9dd6f492bd3f25f79f7bf5cbe0388e/href">https://medium.com/media/8b9dd6f492bd3f25f79f7bf5cbe0388e/href</a> UDF Background UDF pattern, a.k.a. or was originally popularized in web development with Facebook’s and state management, and UI libraries. Early Android can be found from and at in 2015. ’s talks on Rx educated developers on which are fundamental to UDF. Unidirectional Data State Flow React Readux Flux experimentation Brian Egan Guillaume Lung SoundCloud Jake Wharton reactive programming Growing Number of Android Apps Adopting To name a few… and @ Kaushik Gopal Laimonas Turauskas Instacart @ Robinhood Dan Hill @ / Lyft Donald Chen Instagram Engineering @ Microsoft Cesar Valiente App companies adopting UDF Model View View Model — MVVM Gem Lake, Yosemite Emigrant Wilderness Trail For the first iteration of Coinverse I used the (MVVM) approach. MVVM separates the UI from the business logic, improving readability and organization. However, as an app grows with MVVM it becomes a lake of data. Information flows in, out, and around at many points via and with , and . This adds complexity for keeping track of logic, debugging, and testing, which requires mocking many components. Model View View Model Activities Fragments Data Binding, ViewModels Repositories UDF Advantages pc — , Waterfall at Yosemite National Park Ned Scher UDF is a waterfall, information flows in one direction through a single source providing many benefits. One point of entry for streams — The UI and business logic interact through single points of entry. Control UI involving async events — Know exactly when and where things begin and finish Debug issues — Easy to follow sequence of events and identify errors Streamlined tests — The majority of logic is contained in the ViewModel requiring less mocking. LiveData LiveData provides a straightforward approach to implementing UDF. Lifecycle aware Can emit multiple or single events Concise code Quick to implement <a href="https://medium.com/media/aece5d95a459fab51fe4afcff559f91b/href">https://medium.com/media/aece5d95a459fab51fe4afcff559f91b/href</a> View State, Events, and Effects ViewState Coinverse’s main newsfeed View is responsible for holding the final view’s persisted data. This entails all of the content shown to a user on a screen, including information about the content such as enabled statuses. state Looking at Coinverse’s main newsfeed above, examples of view include the contents of the toolbar, what timeframe and the feedtype of feed to display, as well as the contentList to populate the feed with. state Events ContentSelected(…) View consist of both user interface and system initiated actions. UI actions include button presses and text input, whereas system actions might be Android lifecycle events and screen rotation. events In the case of content selected above a view is created, ContentSelected. The will share information with the business layer to initiate the retrieval of the audiocast selected. The Coinbase Blog’s event event Effects ContentSwiped(…) View are one time UI occurrences that don’t persist. include navigation, dialogs, and toasts. are created by the business layer to initiate changes in the UI. effects Effects Effects When the item above is swiped right, the business layer adds a label to the content. The business layer sends an , ContentSwiped**,** informing the UI of the change in the content’s label. The UI can then remove the content from the main newsfeed. CCN saved effect App Structure Let’s understand how the one-way flow of data is structured. The handles all UI and system level actions stored in a single stream. The stream is sent to the that receives the actions and handles them accordingly in the business logic. View ViewModel The is the source of truth for the view and creates any necessary . The also handles requests to the data layer, managing the results , success ( ), and states returned from the with an Lce object (more on Lce's below). ViewModel state effects ViewModel Repository loading content error Repository Both the and are observed by the updating any changes from the in real-time. state effects View, ViewModel Implementation mainfeed loading We’ll use Coinverse’s main newsfeed loading as our example for how to implement UDF. Step 1 of 6 — Define Models <a href="https://medium.com/media/e3e457ec723be5f770f4c050214571d0/href">https://medium.com/media/e3e457ec723be5f770f4c050214571d0/href</a> View Stored as a LiveData object in the storing the contentList of LiveData type state — ViewModel, View and — Use Kotlin’s to pass one time events event effect Sealed class The view uses LiveData because it’s important the data is immutable vs. MutableLiveData. Otherwise, the flow of data would not be unidirectional, and the state could be changed in many places. state View and are not persisted in the . A Sealed class, like an Enum, but on a class level, is used to pass information. Sealed classes define a parent and child class with or without data. TheScreenLoad is a data class with data about what the should load. Whereas the UpdateAds is a class without data telling the view to update the ads in the newsfeed. events effects ViewModel event ViewModel effect Step 2 of 6 — Pass events to ViewModel <a href="https://medium.com/media/21b7345af796983c640793c3fbfdf7ee/href">https://medium.com/media/21b7345af796983c640793c3fbfdf7ee/href</a> In this example, when the system action of onCreate occurs, a ScreenLoad event is added to the stream of view events and sent from the to the to start creating the main feed. Fragment ViewModel All of the events created in the / are added to a object _viewEvent, a MutableLiveData object which updates the immutable LiveData object. I’m using the pattern of passing all of the events in onResume based on ’s . View Fragment LiveData Kaushik sample The LiveData stores data wrapped in an Event. As explained by in his , events ensure a single unique object is added to a stream. This avoids the accidental creation of multiple objects for a single action. Jose LiveData post about events Step 3 of 6 — Process events mainfeed loaded <a href="https://medium.com/media/1e82719128462cbbc400fb2ceab73db4/href">https://medium.com/media/1e82719128462cbbc400fb2ceab73db4/href</a> The receives incoming events, handling each event in a when statement based on the type of ViewEvent class. For ScreenLoad, the entireViewState is updated with the required data. To populate the newsfeed a request to the with getMainFeed is made. ViewModel Sealed Repository Update State Value In cases where an attribute of the ViewState needs to be updated rather than the entire ViewState, Kotlin’s shallow function is useful. copy <a href="https://medium.com/media/ce1bf00297c228e57a7216047f915529/href">https://medium.com/media/ce1bf00297c228e57a7216047f915529/href</a> Step 4 of 6 — Manage Network Requests with LCE Pattern <a href="https://medium.com/media/abb3971ab92689aa13784def4501b3e3/href">https://medium.com/media/abb3971ab92689aa13784def4501b3e3/href</a> To manage network requests, introduces the Lce class object with three states, , , and . The state represents a successful request. Kaushik Sealed loading content error content <a href="https://medium.com/media/115cf44f615dca588145ff82c08df8f1/href">https://medium.com/media/115cf44f615dca588145ff82c08df8f1/href</a> A class is also useful for returning different types of results. Sealed <a href="https://medium.com/media/0282a893889f04b825cc9f73d6a231fb/href">https://medium.com/media/0282a893889f04b825cc9f73d6a231fb/href</a> getMainFeed’s network request shares send the Lce states to the via the LiveData stream. The PagedListResult class can be passed into the Lce for both the and states. The will then manage each state appropriately. ViewModel content error ViewModel Step 5 of 6 — Handle LCE States mainfeed error The gif shows something has gone awry. We’ll see how the error is handled in the . ViewModel <a href="https://medium.com/media/2e31d1bd3a6304c6a19b4728f9102597/href">https://medium.com/media/2e31d1bd3a6304c6a19b4728f9102597/href</a> UDF has streamlined both methods of requesting new content from the network and retrieving the updated content from the database. Prior to using UDF, Coinverse called two repository methods separately to populate the main newsfeed. Room When the feed is loading the existing Room database content is returned so that the user is not staring at an empty screen. For the successful Content case, the updated content from Room is returned. For errors requesting new content, the existing Room content is also displayed similar to theLoading case. In the error above, a SnackBar view is passed to the to display the error message. effect Fragment The observes each Lce state with a LiveData SwitchMap. The SwitchMap passes in one LiveData object and returns a new and different LiveData object that is saved to the view . ViewModel state Like all LiveData, a SwitchMap must be observed in the view in order for the value to be emitted within the map inside the . ViewModel Step 6 of 6 — Observe State Change! <a href="https://medium.com/media/503f19392a8e77dba776d7ab367549a1/href">https://medium.com/media/503f19392a8e77dba776d7ab367549a1/href</a> Now the view may be observed when an update occurs. The view are observed in the same way. state effects Bonus — Removing Adjacent Ads auto adjacent ads detection In addition to a streamlined newsfeed above, UDF has improved how Twitter’s native ads are shown in the newsfeed. MoPub’s MoPubRecyclerAdapter does not have a built-in approach to avoid adjacent ads from showing. Content can be swiped to be saved or dismissed, eventually causing two ads to appear next to each other. Prior to UDF, this was handled with a manual swipe-to-refresh by the user. MoPub With UDF there is a contentLabeled view . When the status of the view changes, meaning an item is labeled to be removed from the main feed, a check for adjacent ads is made. If removing the content creates adjacent ads, the ads are automatically refreshed. state state Trade-Offs Using LiveData for the Unidirectional Data Flow has been great, but it’s not perfect. LiveData is only applicable for logic impacting the UI. For non-UI logic LiveData will not be observed since it requires lifecycle to be passed in. For these instances, or Rx may be used. If no UI is involved then an even better solution might be to offload the logic completely to the backend with . Kotlin coroutines Firebase Cloud Functions There’s not as much customization with LiveData for things like threading. With this year’s latest Google I/O updates coroutines appear to easily integrate with LiveData offering more customization. Coinverse Next Steps Expanding to the rest of Coinverse’s app Unidirectional Data Flow — — Now that the majority of the newsfeed logic is modularized in the , JUnit testing will be easier with less mocking of components. JUnit testing ViewModel Exploring integration with LiveData Kotlin coroutines — Resources Slides — @ Unidirectional Data Flow por Adam Hurwitz Medellín Android MeetUp Notes — Unidirectional Data Flow guide I’d love your feedback on the beta! Coinverse to be updated on a Unidirectional Data Flow sample app and more. Follow me A to and of the for organizing the talk! If you are in Medellín I recommend stopping by their MeetUp. big thanks Cristián Gomez Carlos Daniel Medellín Android MeetUp Medellín Android talk — Unidirectional Data Flow por Adam Hurwitz

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The youth of the 1980s was enthralled by computer games. Playing Batman: The Caped Crusader, and kicking Joker’s ass, was part of the experience for me. But there’s a movement, the Quantum computing world — how will it shape gaming in the future? Green-Screen Nostalgia When I think of nostalgia, I don’t think about the internet or playing soccer with my friends or watching on TV on a Saturday afternoon. Nostalgia, and I’m talking about the pure kind, was playing computer games. Back in 1985 or ‘86, my grandfather bought me a second-hand Amstrad CPC 64k personal computer. It was a machine that I cherished for many years until I dropped it one day. The A-Team Oh, my youth, how I miss you. Source: ebay Back then, when movie hit the theatres, Duran Duran was dominating the airwaves and the Cold War threatened world peace, I remember sitting in front of my PC for hours playing games like Yie Ar Kung-Fu, Chuckie Egg, Ghostbusters and who can forget Bruce Lee? The Goonies ‘I recently learned something quite interesting about video games. Many young people have developed incredible hand, eye, and brain coordination in playing these games. The air force believes these kids will be our outstanding pilots should they fly our jets.’ — Ronald Reagan, 1983 My parents, Luddites then and Luddites now, were forever looking at me in front of the black curiosity with a green-screen monitor that had taken their son’s attention away and stopped him from doing his chores and homework on a regular basis. Yippee, I thought. Before I’d fallen in love with computers, though (or more importantly computer games), I’d been an avid reader. I’d read a book or two a week: Young adult stuff and enough history books to keep me occupied for eternity. Computer games took all that away, at least for the most part of the 1980s and early 1990s. The Underground Railway, Black Beauty, Animal Farm. Then I became an adult and got my reading mojo back. But the nostalgia of those times has stayed with me. As a fan of popular culture, I can see now how computers and the geek culture have contributed to that genre immeasurably. Without companies like Atari and people like Steve Jobs and Steve Wozniak, maybe the world of tech would have been delayed by a decade or two — or at the very least a few years — leaving my childhood a cold, black-hole-like vacuum without the mesmeric attraction of prehistoric pixels on the monitor screen and the three-minute orchestration of beeps and screeches as the game loaded up in the cassette player. What would life have been like then? Let’s not talk about it. Photo by on Magnus Reiß Unsplash Since the massive machines of the early years of the computer dawn in the 60s came about in university campuses, controlled by people in dickie-bows, horned-rimmed glasses and an awkward fashion sense, there has been an exponential growth in their power and ability to entertain. At first, they were expensive and out of reach to the pockets of your average teenager (and their parents). But then things changed by the end of the 1970s. The democratization of computing began. The PC was born. The game consoles, too. And then there were the games that came with them. They were a gamechanger. They created a social ambiance between machine and user. Playing them became a social event, a way of making friends and bonding. What could be better? I’m Not a Hacker Okay, they had a serious angle, too, like for hacking. Who can forget 1983’s with a youthful Matthew Broderick as lead — for me at least, it was the catalyst for my brief career as a hacker when one of my more tech-savvy friends, who owned a ZX Spectrum, got hold of a modem. We were no hackers and connected neither to the WOPR supercomputer or any other for that matter. WarGames very Retirement came early. The ways the technological landscape changed in the 1980s was unbelievable. ‘I burned through all of my extra lives in a matter of minutes, and my two least-favorite words appeared on the screen: GAME OVER.’ — Ernest Cline Roll on thirty-five years or so, and those computers seem funny in 2019. Sixty-four K of processing power won’t do a fart today but back then it was impressive enough. Moore’s Law has been good to computers. But it’s had its time. There’s a new shift in power that will alter the technological world irrevocably. The chronological line from those first games to the Web was a massive step. Yet now we are moving into a new realm, one far from what most people can get their heads around, quantum computing. With it, the era that I remember so fondly is disappearing. That is not to say video games are a thing of the past. No — the games industry is growing, with the market set to to be worth more than 90 billion U.S. dollars by the year 2020, from nearly 78.61 billion in 2017 , according to reports from Best The News. Yet what will they look like in the future? Quanto World, Baby! You’ve switched on your quantum computer, a work of art if ever you’ve seen one — not quite a Michelangelo as a piece of art, but not far off. More verging on the creativity of a Jony Ives of Apple fame. But sorry, you haven’t ‘switched on’ but ‘done’ whatever you have to do to gain access to it. You’ve got a powerful machine, too, ten, twenty, maybe even fifty superconducting quantum bits that are in the processor. But who’s counting? It’s cold, too, chillingly so, at almost absolute zero, technically zero kelvin — that’s roughly minus 273 Celsius if you’re a European or minus 459.67 degrees Fahrenheit if you’re stateside. Again, who’s counting? Certainly not me. Well, you’re all ready to play and you press the start button (if there’s a start button). A quantum computer. Source: digitaltrends.com This is a quantum computer. It’s here to solve all the problems the classical computer can’t. But how does it work? Tiny particles, of the quantum world, have replaced the binary system of ones and zeros. Computer logic has been erased. A new modality has come into its place. Although scientists are trying to create games for these promising machines that add up at the moment to coin flipping and other simple probabilistic games, in the future computer scientists and physicists aspire to fashion games which not only offer visceral entertainment for the user, but also give them the chance to move around in an AI world as real as the one they live in. Already there are companies which have started putting out basic quantum computers possessing a minimum number of qubit power. Unfortunately, these machines are susceptible to errors at the moment. Giants like Google, Intel, IBM and Microsoft lead the way with their billion-dollar budgets and worldwide influence. Yet there are other companies, with minute budgets in comparison but with no less ambition like , IonQ and D-Wave which believe their systems are just as good. Rigetti This battle of corporations versus the indie clan in quantum computers will be interesting. The contest for the first one to understand the difficult physics behind how the quantum world operates won’t be solved any time soon. However, just the idea that there are researchers and companies trying to change the status quo is exciting. We shouldn’t scold the constructing of simple probabilistic games because at least it’s a start, just like the early computer programmers in the mid-20th century started with their games: Josef Kates’ Bertie the Brain, recorded as the first ever computer game in 1950, led us to Pong, which brought us to Return to Castle Wolfenstein in the early noughties and then to Red Dead Redemption 2, in 2018. Go figure. What makes, then, a quantum computer so very different from a regular computer? First of all, a classical computer can’t factor very large numbers. Additionally, they’re not the best at simulating the behaviour of subatomic particles, as this operation would need too many binary ‘bits’ to pull it off. Quantum computers, meanwhile, could solve these problems (or are they not problems to them but mere trifles) using qubits, which are basically quantum bits. Qubits work, not like regular bits — which operate in the binary system of 0 and 1 — but somewhere in between, in what is called in quantum theory parlance ‘superposition’ This means, in essence, that they can be both 0 and 1 at the same time, or partly both. Spooky, yeah? That’s what Einstein thought, too. But there’s more — ‘entanglement’, that’s when two qubits, separated by spatial reality, can somehow influence each other no matter how far away they are from each other. Now that’s kinda spooky! The Conjuring But not as spooky as the picture below: Photo by on Lucas Ludwig Unsplash With this system, quantum computers will be able to factor very high numbers in a much shorter time than even the most powerful supercomputers we have today. Moreover, they will — in theory at least — replicate how atoms interconnect with each other, which could help us create new molecules, or even, unbelievably, new materials never before seen on earth. In artificial intelligence (AI), too, the prospects look good, with companies researching how quantum computers could improve and enhance AI. Unfortunately, though, the realization of these things is in the distance, as quantum computers are still at the elementary stage of development. One of the fundamental flaws of quantum computers is with the qubits themselves. Sadly, qubits are very sensitive things and any interference from external factors can deprive them of their quantum essence, which turns them into normal bits. Boring! This hasn’t stopped computer scientists creating simple games for the machines, though. The Brains of the Games Dr , a Research Staff Member at the Zurich Research Laboratory, an employee of IBM and fellow writer on all things quantum computers on Medium, has created a game called Decodoku, which is an early game based on quantum error correction theory, though it was made on a classical computer. It is a simple game to play. Quantum Battleships is yet another game by the British physicist, again rudimentary in its application but like Decodoku, it’s a start. James Wootton ‘I do not like it, and I am sorry I ever had anything to do with it.’ — Erwin Schrödinger One more game, or rather ‘experience’, is Quantum Experience, by Wootton’s employer, IBM, which enables people to write their own programs on its five-qubit computer. To do it, so IBM states, users do not need a science degree nor any substantial knowledge of quantum physics. That counts me in, then. These text-based games are only the beginning of something that, one day, will be big news. Dekodoku, Dr James Wootton brainwork. Source: Twitter Text-based games are all well and good, but what is the future of video games when the technology of quantum principles is relatively advanced enough to head into that direction? Most in the industry believe classical computers will still carry out the donkey work of the video game interface like graphics, sound and movement, while quantum computers could quite possibly, if predictions are correct, enhance the physics engine of the games and mechanics of movement, giving the visualization of the experience a more visceral feel. In actual gameplay, however, quantum principles could add weirdness to games never seen before, such as characters or adversaries who can, quite strangely, act out their quantum ability to be in two places simultaneously, or existing and not, influencing each other through entanglement and behaving against the laws of space and time. This could be a boon for hardcore gamers: Randomness, it seems, goes hand in hand with video games and how they play out. Perceptive gamers, however, quite often after playing a game many times, begin to see patterns of behaviour within the game’s processor algorithm that are not so arbitrary, detracting from the ultimate randomness and ultimately, the gameplay experience. ‘Quantum mechanics makes absolutely no sense.’ — Roger Penrose Adding quantum principles to a game’s algorithms could truly create random gameplay, and add never-before-seen playable environments and infinitely unique character behaviour. Yet it’s all supposition, and who really knows? One thing that isn’t supposition, though, is to reach this point it will take a lot of hard work and harnessing the intelligence of people in the industry, getting companies like Rigetti, IBM and others to work together in focussing their efforts on improving quantum algorithms. Up to now, all the talk — and quite rightly so, too — has been about the software challenges. Still, the conundrum of hardware and the complexities we could face building it, cannot be ignored. Qubit power — and what would be needed to outperform the operational efficiency of classical computers — is another area of some contention between experts in the industry. The standard amount bandied about by most is fifty qubits, though it has been reported that a quantum machine of at least 1,000 qubits is the optimal amount to accommodate inbuilt errors within the system. We even have a claim — outlandish, no doubt — by Vancouver-based D-Wave of a fully-operational . To achieve this number of qubits is going to take a superhuman effort. If Norishige Morimoto’s — global vice-president of IBM — thoughts of a global ‘quantum supremacy’ are to be realized soon, time is running out. 5000-qubit computer by 2020 Are they dreamers or realists of the quantum computer’s future? Whatever happens, the future of video games and how they evolve will be an interesting one. With quantum computers’ ongoing development, and computer scientists and quantum theorists working flat out to evolve the discipline, exciting times are certainly ahead. The Joker controlled by quantum mechanics? Sounds interesting, if a little unfair for gamers. Don’t think I’m gonna beat him, though! <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

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Imagine a human-like teacher with no human flaws. The best educators in the world sometimes suffer from innate human errors, taking different forms in every one of us. They will eventually grow tired and nervous. Not even the best of them can provide personal attention to a class of 30. So, robots then? Isn’t new technology an obvious solution for this? Computers never sleep; the knowledge they impart is available 24/7 across continents, time zones, and devices. They can teach hundreds of students at the same time without ever making a single mistake. The algorithm rarely does — just ask Siri or Cortana about it. According to the AI Market in the US Education Sector report, we can expect that artificial intelligence in US education between 2017 and 2021. We’re almost there. It’s about time we rethink education in the age of AI and create a teacher with no flaw. will grow by 47,5% Right? AI Is the Next Step. But Where From? Artificial intelligence is the future of education. There’s no doubt about it. Companies big and small are making a leap forward into AI-driven educational tech, with entire countries investing billions of dollars into further development. And, pardon my pun, they’re not making an uneducated guess — AI is already here, working like a charm. Take a closer look at the current state of education, and you’ll see why. Today’s generations are not only tech-savvy. They are what we call digital natives. Everywhere in the world, these kids and young adults have access to the internet, smartphones, and social media, but there’s only a handful of classrooms that have accepted new technology. On the one side, we have limitless connectivity and almost aggressive digitalization, while on the other, we have decades-old textbooks and smartphone bans. The result feels absurd at times — students, empowered by digital assistants, knowing more than their teachers. The change is due, with every aspect of the traditional classroom being up for reimagining. Teachers and Tech Collaborating? Of course, getting rid of flawed human teachers is not the answer. Not only because we’re not ready for a futuristic scenario that excludes the human element from a very human field of study, but primarily because continual education doesn’t equate with short-time knowledge acquisition. Learning and googling a fact are not the same things. Yes, we’re talking about the emotional component of education. You can’t have good pedagogy without empathy, and that’s a fact. The need to help someone grow and succeed is just as deeply rooted in us humans as our mistakes are. The best teachers are a rare combination of this need, paired with expertise, experience, and methodology. We can program AI to know all the answers, just like Google does, and we can feed its algorithm with the most effective teaching methods, but we cannot make it care. Not yet. And that’s why instead of talking about AI instead of teachers, we should talk about them collaborating. And as expected, AI is brilliant at the administration level. Making no mistakes and never growing tired, artificial intelligence is already helping teachers grade exams, assess homework, provide helpful answers to students, and much more. Their ability to streamline admin tasks drives efficiency and frees up teachers’ valuable time. And that’s how teachers can provide personal attention to a class of 30. Artificial Intelligence for Tutoring In addition to doing admin tasks, AI can deepen its impact on education by picking up where teachers leave off — after class. Artificial intelligence for tutoring is possible because the capability called machine learning (ML) allows it to become smarter with every new interaction. As homework and test preparation aid, AI can be of immense help. Fearing that they might make an embarrassing mistake, students would often rather avoid reaching out to teachers for after-class consultation or test prep than gather up the courage to admit there’s something they don’t understand. Teachers can be judgy, but technology can’t. Let’s take AI-driven chatbots as an example. In this scenario, ML-based chatbots can provide students with answers far more complex than class schedule or exam reminders. can be very advanced, ranging from intelligent campus reps to actual after-clas tutors that solve student problems in real time. Education chatbots In a myriad of different capacities, chatbots can help students figure out placement plans, reduce the time it takes students to find courses, provide course feedback, teaching-assistant style advice, and study notes, even deliver tests and results to remote and self-taught students. According to Campus Technology, chatbots are the leading tech for 2019. Personalized & Self-Paced Learning With AI, every aspect of the traditional classroom is up for reimagining. For example, will future students know what the four-wall classroom is at all? The way education is looking right now, it seems like eLearning will not stay a mere trend for long. Together with AI and other emerging technologies, learning as a digital service is transforming the way we approach knowledge acquisition. It’s all about personalization nowadays. There are many obvious advantages of eLearning over traditional education, starting with convenient new tech-supported techniques such as mLearning and microlearning. Then there are other popular methods like gamification and social learning, made to battle the engagement crisis. And, students don’t have to wait for digital academies to modernize. They are already at the forefront of modern-day education, from where they are addressing some of the greatest needs of digitally-native students. Personalized learning is among the main ones, and AI is well-equipped to provide customized learning paths to students as individuals. This is another innovative use of machine learning, only this time it doesn’t help AI learn more about the subject matter but about students themselves. By tracking their learning behavior and tracking their progress, AI is becoming something of an expert in a single individual student. The result is a highly personalized learning experience that cannot fail. Building Self-Improving Classrooms But that’s still not the key selling point of educational AI. For the purposes of education, AI’s ability to provide real-time feedback is beyond comparison. Education is based on feedback; that’s why we have teachers. To learn — and to learn correctly — students must have somebody to follow their progress, point out their mistakes, solve their dilemmas, and give motivational criticism in order to improve performance. The problem is, they can’t promise that to every student. Artificial intelligence can. Real-time feedback has implications beyond teacher-student tutoring. AI can give valuable input on teachers and courses themselves, thus helping educators bridge knowledge gaps and improve the learning environment in a matter of seconds. Nothing is more important than this. Fixing our mistakes is how we learn, after all. What AI does is help us do that mind-blowingly fast. Education Will Keep Getting Better The possibilities of educational AI are tremendous. In addition to its ability to automate basic activities, adapt curriculums to individual students, and provide additional support in the form of tutors, AI, chatbots, and other AI-based technology can do a lot more for the future of education. Like, write smart content, for instance. Global accessibility is another major benefit of its implementation. Through eLearning and blended learning, AI offers equal learning opportunities to every student in the world, including those who speak different languages or have visual or hearing impairment. All students who must be home-schooled can receive a world-class education. No, modern-day students don’t need flawless teachers. What they need is already here — technology that can optimize, personalize, accelerate, and augment our current educational efforts. Being tireless and extremely insightful, but also growing smarter with every new interaction, artificial intelligence makes the perfect candidate. <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

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Big data is a big problem, at least getting anything useful out of it. there is about three quintillion (the next step up is sextillion or one zettabyte) bytes of data created and only about 20% of it is structured and available to easily process. Nearly all useful processing that is done relies on a philosophy that is little changed from the green bar reports we were generating during the night shift and handing out up till the turn of the century. The whole map/reduce process is overnight batch processing, you aren’t working on live data, you are working on a snapshot, which might be fine for some companies, but for others, they need to be able to make decisions on high-velocity inbound data in near/real time. Every day Your typical relational database uses a b-tree index and these are generally good for retrieving smaller subsets of data from large sets of data. They are notoriously slow when adding data as the indexes get populated, although fractal indexes like make the put process faster. This brings me to bitmap indexes, which are a radical departure from the type of indexing you are used to. They are incredibly fast and performing searches that have to combine queries using multiple columns that have low cardinality such as months of the year or shoe size as opposed to something like the distance you live from the North Pole, which has a massive number of variations. The issue with bitmap indexes has historically been that they haven’t scaled well, which brings me to . TokuDB Pilosa Pilosa is an open source bitmap indexing that is now maintained by , the same people that originally developed it while at and have gone through some organizational changes. Inspired by a conversation at a conference where the topic of bitmap indexes came up, there was a realization that this could solve a lot of their problems with massive amounts of high-velocity data that needed to be indexed and analyzed. The group spent the next several years building and perfecting their technology which resulted in 8 patents thus far (yes, it is still open source though). project Molecula Umbel Cassandra Using a data repository, cloud, and operating system agnostic approach, Pilosa acts as an additional indexing system that can be applied to an existing repository, including a flat file. Looking at the example below, if you imagine that the column headers represent Customer_ID, and the rows in Field A indicate product inventory, then each 1 represents the products purchased by each Customer_ID.. You will also notice that this is separated into shards, this is how Pilosa scales horizontally so effectively and was one of the huge technical hurdles that they solved with the project. These bitmaps are very small and are compressed in addition to that and do not need to be decompressed to read. Pilosa’s software can store a knowledge representation of underlying data in memory, making it orders of magnitude smaller and unimaginably fast. Keeping that agnostic approach in mind, you can actually have Pilosa index across data repositories to tie together disparate data sources and allow you to execute those very fast queries against all of them at once. The implications of that are really staggering when you think about it for a moment, with a performance that claims that a simple query can traverse more than 2 billion edges in one second on commodity cloud hardware, there is no reason you can’t do your critical analytics in real time. (This graph is from 2017 and there have been significant improvements since then) Some really clever applications of this technology is the ability to create a virtual data source from your underlying data and share it with a client or partner by giving them access to an API and now they can return index data, something like all XL t-shirts sold in August in Los Angeles but without giving them access to the underlying data. The implications here are tremendous, think about being able to aggregate out data from a lot of different companies in the same space into an anonymized repository that you could report on? This is another unique and powerful feature that I don’t recall seeing anywhere, Molecula refers to it as being the VMWare of data. Now, I will say that their documentation could use some beefing up, but you can get a learn about their schema language and their query language ( ) is not anything like you are used to, simple but different, and therein lies the challenge, (re)writing your code to take advantage of it. Pilosa isn’t terribly hard to install and setup, but you really want to think about what you are doing and plan it out so you are taking full advantage of it. The index rate is insanely fast, depending on the data density, you could do a billion records in a half hour because it isn’t making copies of the data. The issue is that you need to rearchitect your code to make use of it, they have available for Golang, Java, and Python. Having to redo your code isn’t necessarily a bad thing, but it is a time-consuming thing and a lot of people would love to see a magical drop-in replacement that makes all their code run faster. Molecula has set out to solve many of these challenges, with one focus being a plugin for Kafka so that you are only dealing with that pipe instead of a huge number of programs. here PQL client libraries This system is ripe for a partnership with a data visualization dashboard, it would make a great combination for a business to have a live dashboard into certain types of data that have high volatility. There is not really a point in using Pilosa until you are in the 4tb range at a minimum, so keep that in mind. If you want to learn more about bitmap indexes, I really enjoyed . this article

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When you can’t control what you can’t control but you also can’t look away, sometimes the phone is a crutch... to look away... in this time... when I looked at the phone... you all provided another way to look at things.... education, discussion... when the solution is not in sight, more ideas are great to see... the ideas were all over the place... the reality is that in ICU the family members can’t do much... but when anyone believes they can help, they actually do help more (than if they didn’t believe they could help)... so your messages and thoughtfulness gave me that... kind regards...

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Machines may not have taken over the world yet, but they are seeping into our lives and making it better. AI is changing every aspect of our lives. From self-driving cars to talking bots, there are so many examples of AI in use today. The technology is disrupting so many industries, and the travel industry isn’t exempt from this. Gone are the days when you had to count on a travel agent to plan your next vacation. You no longer need to engage yourself in that tiresome chain of conversations with your agent for your travel arrangements. AI now infiltrates every aspect of the travel industry. With the help of this technology, you can get personalized and intelligent travel solution tailored according to your needs. Google Flights is already taking advantage of and announcing them even before the airlines. But this is only a fraction of what AI can do. From data processing to improved customer service, there’s a lot that this technology can accomplish. Let’s see how AI is revolutionizing the travel industry. AI in predicting flight delays Trip Planning According to booking.com, are comfortable letting a computer plan their next trip based on their travel history while 50 percent of travelers say that it doesn’t really matter who’s planning the trip as long as all their questions are answered. No matter if it’s a romantic trip to Paris or you’re climbing Kilimanjaro for a thrill, a computer can plan everything for you. Brands can also offer tailored recommendations to travelers based on their needs using AI technology. 29 percent of the travelers TUI Group and Utrip have already created a personalized that asks users to rate their preferences across various categories such as “Food & Drink,” “Nature,” etc. and sorts through millions of options to deliver personalized itineraries. You will get a list of places to visit, activities to try, and more. trip planning service You can always add more activities for a better experience. For people who find it hard to spare time to plan their next visit, these tools can be very helpful indeed. Improved Customer Experience One of the most exciting applications of AI in travel is assisting customers online. Customers don’t like to wait around, and chatbots can improve customer experience by answering to their queries instantaneously. With the help of AI, businesses can operate 24*7. The demand for smarter hotel rooms has been on the rise. Advanced AI-powered assistants can carry out guests’ requests, offer real-time recommendations, and more. IBM’s , which focuses on hospitality and automotive industries, can be very helpful for hotels to meet the demand for smarter rooms. Watson Assistant Unlike Alexa, Watson Assistant does not capture the user data. So, hotels need not worry about user data. The Cosmopolitan Hotel in Las Vegas has introduced a with a flirty personality. Rose provides recommendations for bars and restaurants inside The Cosmopolitan. It can also play games with you in case you’re getting bored. There are many other chatbots in hotels around the world, helping customers in several ways. chatbot named Rose Data Processing and Analysis The applications of AI are not limited to customer service alone. Artificial Intelligence can be used for data gathering and analysis. Using this data, various conclusions can be drawn about customers, pricing strategies, etc. It allows businesses within the industry to boost conversions and make better business plans. AI can sort through massive amounts of data quickly and efficiently. The has begun using AI to identify what their customers want. The AI system reads thousands of customer reviews online and finds out what matters to the customers. This process would generally take days if not weeks if it were to be done by a human. Implementation of AI will help make strategic decisions about guest behavior, and automate many processes to enhance business performance. Dorchester Collection hotel chain Increased Productivity Aeromexico has been using Facebook Messenger bot to respond to basic user queries. The idea was to cater to which were repetitive such as “is there a charge for checking a bag?” The bot answers the queries with 500 common responses. No human interaction was required whatsoever. Airlines, in general, can benefit a lot from these bots. Lufthansa’s bot can help customers find the cheapest flights. KLM Royal Dutch Airlines also employs AI technology to respond to users’ queries on Twitter, Facebook, etc. It allows airlines to utilize their workforce for creative tasks that require human attention. 80 percent of the user questions Mildred Online Reputation Management People trust online reviews more than anything else. Therefore, it is essential to keep an eye on what customers have to say about your business. A negative review can hurt businesses. Using artificial intelligence, companies can monitor customer reviews, social media comments, and other mentions about the brand. Hotels and airlines can respond to negative feedback, observe the trends, and get a better idea about what the users want. This can be beneficial in boosting sales and profits. AI is making our life so much easier. The day is not far when we don’t have to wait in queues at airport check-ins. The idea is to use AI wisely so that it helps humans in a variety of tasks within the travel ecosphere rather than replacing them.

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MimbleWimble is a scalable, privacy-based blockchain protocol that verifies transactions without needing to store the entire history of the chain. What is MimbleWimble? MimbleWimble began as a gleam in the eye of anonymous developers in July 2016, who published a text file titled “ ” that contained plans for a new blockchain protocol that promised to bring Bitcoin back to its cypherpunk roots. The idea was based around scalability and anonymity and it caught the attention of others in the cryptocurrency space. Within a few months, the coding for the MimbleWimble protocol had begun. MimbleWimble Now, 2.5 years later, two networks designed to test MimbleWimble have finally launched their mainnets. This article focusses mainly on one of them: Grin, which has generated a lot of attention within the crypto community. What is Grin? Grin is an implementation of MimbleWimble launched in January 2019. It is written in Rust and uses two versions of Cuckoo Cycle as its PoW algorithm (an ASIC-resistant one and an ASIC-friendly one). Unlike Bitcoin, there is no public ledger. The network uses no addresses and encrypts all amounts. However, Grin relies on Bitcoin’s CoinJoin method to achieve scalability and privacy. In the words of its , Grin is designed to be simple, fast (one block per minute), and hold a fixed block reward of 60 Grins over time with a decreasing dilution. It is not trying to compete with Bitcoin or Ethereum. It is trying to offer an alternative. Here is a Grin . anonymous developers explorer Genesis of Grin MimbleWimble was born in August 2016 when a user with the pseudonym “majorplayer” linked to a file in the IRC channel #bitcoin-wizards that described MimbleWimble as a private and scalable upgrade for Bitcoin. Cryptography researchers took notice, including Blockstream’s Andrew Poelstra, who wrote a for MimbleWimble later that year. more technical whitepaper Originally, the project was intended to be a sidechain for Bitcoin. However, Bitcoin’s scripting system presented limitations, and in to launch a new cryptocurrency: Grin. mid-2017 the developers decided Grin’s developers seek to remain true to cypherpunk principals. The project was funded entirely by the community. There was no ICO, no airdrops, and no pre-mining. “ ,” according to Igno Peverell, Grin’s pseudonymous lead developer. No funny business Grin coin went live on January 15th, 2019, almost two weeks the launch of its competing implementation, Beam. after Grin vs Beam Grin and Beam are the two different implementations of the MimbleWimble network. While coding for Grin began October 2016 coding for Beam did not begin until March 2018. Nonetheless, the Beam mainnet launched almost two weeks before the Grin mainnet. There is a stark difference between the two projects. Grin represents community-driven decentralization and the original spirit of crypto, while Beam is more corporate-based. For example, Grin uses open source governance, there are no founders’ rewards, and the developers are volunteers working part-time. Meanwhile, Beam is backed by venture capital with a paid, full-time team of developers. Beam also uses part of its 20% block reward to fund development (Grin relies on donations). Other differences between Grin and Beam: The main Grin implementation is in Rust while the main Beam implementation is in C++. Beam uses Equihash, Grin uses Cuckoo Cycle (both ASIC resistant PoW algorithms). The Grin team ASICs are centralizing and unreliable, but also admitted they are unavoidable. Thus, Grin uses two separate proof-of-work algorithms: Cuckatoo31+, which is ASIC-friendly, and Cuckaroo29, which is ASIC-resistant. The proof-of-work balances mining rewards between the two every 24 hours. Technical details are explained . The Cuckoo readme is . has stated in this post here There is a constant, infinite supply of Grin to discourage hoarding. Grin’s block reward is 60 Grins per block, and one block is created per minute — so one Grin is created each second. T this monetary policy was designed to discourage hoarding and improve distribution. In contrast, Beam has a fixed supply of approximately 263m coins. he team states Finally, Grin coin is intended to be a usable currency; whereas Beam places emphasis on being a store of value. Grin and Beam as in friendly competition. Indeed, Beam’s FAQ describes Grin’s team as not “100% focused on the project” and its technical decisions as “less practical.” However, the FAQ adds “That said, we have immense respect for Grin team and wish them success.” It seems to be true. Beam recently . have been described donated BTC to Grin’s security audit fund Photo by emhmark3 A Return to GPU Mining Grin has also been touted as . The a GPU with over “3.7 GB of very fast DRAM”. for updated discussion on this matter. You can check the implementation for a standalone Grin , and official . the coin to bring back GPU mining Grin team recommends Check the Grin forum miner mining stats The Grin mining pool was released by BlockCypher. Soon after launch, Grinmint for their mining pool, 0.5% of which is for the Grin development fund. GrinMint announced a 2.5% fee Criticism of Grin A major limitation of Grin as a cryptocurrency is transactions require both participants to be online at the same time. have suggested they might run their wallets on servers that are continuously on. However, this solution could eventually lead to centralization, which Grin is trying to avoid with its ASIC resistant PoW algorithm. Some people There is also a problem with usability. Grin only offers a command-line wallet (for Linux, OSX and Windows), making it hard to access for the less tech-savvy. In contrast, Beam has a much more user-friendly wallet. Finally, MimbleWimble does not support scripts. Some users consider this a defect since scripts make a blockchain programmable. However, programmability is not one of Grin’s main goals. As , “the design philosophy of Grin is to be as simple as possible.” Nevertheless, Grin’s FAQ page links to scripting could be possible. Poelstra explains messages from 2017 claiming Image by 140782791@N04 Grin’s Target Audience Some say that Grin will be a coin for Bitcoin maximalists more than investors. In other words, people will be drawn to the tech underpinning Grin, instead of the possibility of getting rich. MimbleWimble and Grin’s coding appeal to a tech-savvy community that is already well-aware of cryptocurrencies and their potential. Grin’s landing page has a look reminiscent of cypherpunk. The Harry Potter references in the pseudonyms of the developers and the name “MimbleWimble” itself suggest Grin is for a niche audience. On the other hand, Beam uses branding in a way that is more similar to EOS or TRON and is clearly catering to a more mainstream crypto crowd. Conclusion Grin’s open-source code and grassroots community have caught the interest of those who identify with the original spirit of cypherpunk. Some may see Grin as a throwback to a time when profit was less central to the cryptocurrency space. This is why in crypto forums and in the media Grin is described as everything from cute but ineffective, to the coin that will save Satoshi’s original dream. A mere five months from its release is too soon to tell what its future will be. But without a doubt, many people will be keeping an eye on Grin’s performance. Buy GRIN on Kucoin _channels if you like my articles and would like to know more about blockchain and cryptocurrency projects. Subscribe to my Medium and Twitter If you have any questions about this article, please comment in the comment section below. Thank you!_ <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

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