Whether you see the glass as half full or half empty, I think we can both agree, sometimes it makes sense to just fill the damn cup. And that’s the focus of today’s article, part two in our series on Existential Risk. If you missed that, . check that out here before continuing And now that you’re sufficiently depressed, let’s take that discomfort and channel it towards something productive, solutions. A quick recap, the seven deadly apocalyptic horsemen: Well-intentioned genetic engineering Bioterrorism Climate Change Nuclear War Superintelligent AI Lethal Autonomous Weapons Space-Related Catastrophe And now the all-important question, how to tackle these seemingly insurmountable issues. 1. Well-intentioned genetic engineering If a billionaire or a bold company trying to cure cancer could accidentally wipe out most—or all —of humanity, how do we prepare for the eventual altruism that could kill us all? This will depend on the proliferation and cost of the technology, but it seems obvious to assume genetic engineering and biotech will become increasingly democratized— cheaper, easier, faster… This has certain advantages: individuals everywhere have the ability to counteract wrongs and keep an eye on the status quo. And yet, like a mass murderer in a Texas town filled with .45s, the idiot with the gun always gets off a few shots before someone drops him with a well-placed double tap… Biotech bullets spread much faster. A moratorium on genetic engineering seems equally unlikely, the promises are just too great. So, what do we do? Are these technologies safer hidden in some lab, or open-sourced for all to see? Then again, look at the public outcry against GMOs, primarily due to misinformation. Is involving the public in something as important as our collective defense against the unintended consequences of gene-editing really a good idea? Questionable, at best. But most countries and many companies will want to capitalize on the most transformative industry of this coming century. So, nuclear’s anti-proliferation strategy isn’t going to work. This club is too easy and too important to be a member of. An initial framework: Step 1) An international think tank: Get the smartest biotech (and related) minds together and incentivize them to brainstorm unintended consequences and possible synthetic bio risks, creating a risk index of all forms of genetic and biological research, to be used later in randomized monitoring. Step 2) An international standard MUST be implemented—with threat of retaliation for any who violate the terms — where certain controversial areas — such as chemical/biological/viral weapons and gene drives — are banned and unlike the social media free-for-all, companies and countries are held MASSIVELY responsible for the consequences of their actions. Step 3) Creation of a biotech-specific investigative journalism organization dedicated to uncovering problems with the industry and bring the darkness to light. This is anything but a complete plan, but as the scariest and most probable existential risk, it’s a tough nut to crack. NOTE: 2. Bioterrorism At least terrorism is more straightforward. But, it also suffers from many of the same issues listed above. There are a few saving graces, chief among them being reduced resources and predictability. Even if the Saudi Royal Family — the largest funder of terrorism worldwide— dedicated their entire estimated $1.4T net worth to the pursuit of bioterrorism, that’s only 0.65%  of the $215T wealth of the world. And there are a lot fewer folks to worry about and ways for things to go wrong. Unfortunately, even metal detectors in schools and sports stadiums have proven inadequate at stopping attacks. Would bioweapon detectors be any more effective? A few ideas: Source: CNN Step 1) SSRIs have been implicated in nearly 86% of school shootings ( ), and given the fact that this seems a good place to start. And it would decrease gun crime/school shootings… seems like an obvious win. here's more info with our interview with Dr. Bill Walsh no testing is done prior to prescription, Step 2) The trickier part of the problem are true psychopaths. It only takes one monster to engineer a super virus, and vaccines can take months to develop and mass-produce. If costs come down, it’s fair to assume the mass production part is as genetic/pharma 3D printers would be easily distributed, likely in every pharmacy. It’s even possible everyone owns their own one day. But that’s only half the battle. We need either: to be able to develop vaccines — or cures — exponentially faster, or we need sufficient immunity—or biological resilience — to survive as yet uncreated/imagined diseases and weapons. It’s almost impossible to predict all possible failure modes, so speedier drug/vaccine development is probably our only option — possibly combined with distributed gas-mask-esque protection. For my money, I’d want several international think tanks competitively funded (or something akin to the XPRIZE) with the goal to reach the one or two day mark for diagnosis and vaccine development after disaster strikes. A sufficiently catchy superflu covers most of the world in about a week. Time is ticking. Step 3) In addition to the aforementioned think tanks — working in collaboration with the threat think tanks from #1 — robust incentives for private market solutions need to be addressed. The market viability is currently too early, no one’s willing to pay for prevention until it’s too costly to avoid doing so. This is something governmental venture arms should invest heavily in, seeding the companies that could save all of us. 3. Climate Change Governments have proven ineffective at reigning in the effects of climate change, or our untenable CO2 emissions. This is the tragedy of the commons working its awful game theory. Everyone acts in their own immediate self-interest because everyone else is, and the pains of inaction are small, at least for the time being. There are two schools of thought here: technology will save us and technology is the enemy. Both are right, and wrong. To build a sustainable future, we have to embrace technological solutions to reduce emissions: more efficient farming, electric vehicles, renewable energy; while cutting back on overall consumption. If we’re already at the carrying capacity — the total amount of resource extraction our planet can sustain — allowing 3rd world countries to match our level of wasteful opulence will doom all of us, even if their economies and infrastructures are greener and more renewable than ours. The fact is, we make and consume too much shit. But, there’s an answer, and it doesn’t have to suck. Top-down approaches: Step 1) Courtesy of Douglas Rushkoff: Invert dividends and capital gains tax rates. Today, capital gains are ~10% and dividends taxed at the much higher income tax percentage. This incentivizes companies — and investors — to aim for growth at all costs, profits be damned. Only one thing grows forever, cancer. An economy predicated on growth consumes more and more as it destroys everything around it. By inverting tax rates, companies — and thus investors — could be more focused on sustainable business growth/practices. Apple wouldn’t need an iPhone to wear out every two years, or to slow down your OS to make you upgrade. Instead of an economy of one-and-done, it would incentivize a longer term, more minimalist existence where everyone wins, profits, and less shit ends up in the landfills of life. For more, see this interview. Step 2) : Eliminate subsidies on fossil fuels, cars and high emission industries and create subsidies for development/deployment of renewables. Courtesy of Jarl Jensen This one is pretty straightforward and universally applicable. Renewables are already on par price-wise with fossil fuels and coal in most areas of the world. A little extra push makes the pieces topple faster and drives us towards a sustainable future. If additional subsidies were created for other greener businesses: clean meat (animal agriculture is responsible for ~13-18% of emissions — depending on how you measure and who you source — more even than global transportation), electric vehicles, net-zero construction, etc…, we’d be well on our way to mitigating worse of climate change. Wild cards: Carbon tax — has yet to work/be implemented but may in fact prove necessary. Bottom-up approaches: Step 1) Local agriculture/coops: The farther your food travels, the greater the carbon footprint and chance of spoilage/waste (which is approximately 24% of all food). The future of farming — with the possible exception of lab-grown meat, due to the value of scale — is local. City governments would do well to incentivize local farming, both creating jobs at home, reducing dependence on foreign forces and greatly reducing emissions. Step 2) Local tax hikes on high emission industries/products can ice this cake. Many initiatives are almost impossible on a global or even national basis, but municipalities and states have the ability to pass hard laws and bills that larger legislatures cannot. Some states are already leading the way and more will follow as consumers demand action. 4. Nuclear War For all its faults, mutually assured destruction (MAD), has been a relatively successful strategy, despite many near catastrophes. On the whole, the world has done a good job mitigating — or at least preventing — all-out war up to this point. But, the spread of nuclear power makes this harder daily; much like a country filled with guns, more isn’t merrier. The progression towards renewable energy and away from nuclear — given the accidents at Fukushima — is a positive development, at least from the prospect of nuclear war, but greater efforts MUST be made at disarmament, especially among mutually hostile countries: US/Russia, Pakistan/India, etc… But power is rarely given up, so more stringent monitoring procedures need to be implemented to prevent accidental leakages of technology/weapons, etc… For my part, I’d like to see overhauls of missile command systems. Is a thirty second — or however long the incredibly short snap judgment time is — delay really enough contemplation before hitting the big red button? Twitter would seem to suggest otherwise. 5. Superintelligent AI The consensus among AI researchers is there’s no consensus on if/when we’ll achieve superhuman AI. Assuming we will — a necessary assumption as not doing so would be like riding the Titanic without lifeboats or a vest — it begs the question: how the heck can we contain and/or control the all-powerful mystery that is AI? Thankfully, more and more AI researchers are working on this very problem today — many because they’re terrified of the implications. The best theory we’ve come up with is robust value alignment, ie building artificial intelligences in line with human values and morals. This is problematic at best. What exactly are human values? Sharia Law? The 10 Commandments? Hammurabi’s code? The US Constitution?  If anything, assigning a set of values for an AI to follow is an exercise in folly. HP didn’t break any laws when they made Hitler’s death camp punch cards… Well, we could try to have AI’s learn our values through observation, but data’s biased… and we’re even worse. Or, we could hope a superintelligence would just ignore us, but then again, we accidentally step on ants all the time, and kill the ones who crawl into our homes. So, what can we ants do? The best approaches I’ve seen involve either: limiting the capabilities of AI systems — which again, seems impossible given the risk/reward ratio and tragedy of the commons problem discussed earlier, let alone being able to predict bugs/failures — or counter-defensive AI, ie fantasy sports for society. I sure can’t cover LeBron James or stop a Steph Curry 3-pointer, but with other elite athletes (or AIs) playing on my behalf, at least there’s a fighting chance. Step 1) International regulations on proper development/containment of AI systems. The last thing we need is another NSA-esque leak where XYZ government or corporation accidentally release the end-of-times source code to anyone with an internet connection. Software is pretty easy to repurpose. Step 2) Sizable defense budget allocations to a non-governmental think tank/initiative focused on threat detection/forecasting. The only way to avoid disaster in the uber-exponential world of AI advancement is to plan for failure LONG before it happens. Two seconds is an eternity in ones and zeroes. 6. Lethal Autonomous Weapons It took a dozen terrorists, $500,000 and a few months of planning for Al-Queda to fly 747s into the World Trade Center. The world will never be the same. And yet today, any idiot can buy a drone and attempt to take out a jet or blow up a building, all from the comfort of their couch. Drones are scary, but autonomous drone weaponry is downright terrifying as costs drop and access rises. It only takes one country to develop said tech before others feel forced to play catch up. And how do you stop an autonomous army? Trick question, you don’t. It’s nearly impossible to shoot down —or suicide bomb —a drone. And to date, robust anti-drone solutions have proven ineffective. Democratized mass murder is something humanity CANNOT afford to let happen, there’s no way to put the cat back into the bag with 3D printers, global supply chains and open-source tech. We NEED international efforts, now, before it is too late, with 1st-strike retaliation against any country or company found to be in violation of the accords. I and many others I’ve spoken with are worried anything less would ultimately prove insufficient. 7. Space-Related Catastrophe In theory, easy — put money towards monitoring, fundamental research and design of asteroid path-altering technologies. It doesn’t really matter who does this, or why they do. We’re all on this little blue spaceship together. Unfortunately, every country hopes another will do the job for them. In the US, we’ve massively slashed NASA’s budget and people question the value of space and space exploration. That may mean international agreements are needed to ensure sufficient funding and advancement in the all-important space-missile defense system for all of us. This would be ideal for an ultra-billionaire to start a foundation around… hint, hint Jeff and Elon. Closing Thoughts Existential risks are a tough nut to crack. The list and suggestions above are by no means complete, a million challenges lie around the corner of every idea. But we don’t need perfect solutions to get started, just basic frameworks and smart, committed folks working towards the perfect good. And in a lot of areas, we have that already. And in a lot of ways, that’s you guys. Here’s where I ask for your help. I don’t have the answers, not even close. I just want to get the conversation started. How could we make these plans more robust and idiot-proof, because that’s what it will take. In the game of survival, it pays to rig the deck. Any great ideas? Would love to learn from other more knowledgeable folks, or, if you have any great suggestions for guests, mention them below. Any of these topics would make great episodes. Disruptors podcast Learned something? Share this around and click the 👏 to say “thanks!” and help others find this article. Hold down the clap button if you liked the content! It helps me gain exposure . Clap 50 times and follow me on Twitter: @mattwardio or subscribe to The Disruptors podcast.

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House lawmakers officially ask Facebook to put Libra cryptocurrency project on hold The US House of Representatives, the country’s lawmakers, are becoming more insistent about halting the progress of Facebook’s entry into the cryptocurrency market with its Libra token. It has sent one letter, and now another to Facebook’s executives, calling for a halt to development of the token. Maxine Waters, a Republican congresswoman was the first to call for a moratorium on Libra while lawmakers investigated it. Now four other members of Congress have joined her in sending a letter to Mark Zuckerberg, who must by now dread the arrival of couriers. They have also sent the letter to Facebook’s COO Sheryl Sandberg and Calibra CEO David Marcus. The letter alleges the Libra system, which was registered in Switzerland a few months ago, is intended to rival U.S. monetary policy and the dollar. It says, “This raises serious privacy, trading, national security, and monetary policy concerns for not only Facebook’s over 2 billion users, but also for investors, consumers, and the broader global economy.” The lawmakers also claim the system could become “an under-regulated platform for illicit activity and money laundering.” They say that these concerns arise from he lack of detail in the Libra white paper. And, to further justify their concerns, they refer to the Cambridge Alanlytica scandal in which the political consulting firm accessed Facebook user data to influence voting behaviour in the 2016 election. This has remained a bone of contention since then. Furthermore, the representatives point to the fact that Facebook removed 2.2 billion fake accounts in the first quarter of 2019, and indicate their general scepticism about cryptocurrencies, referring to the amounts stolen by hackers. These are their key reasons for calling a halt to the Libra project and asking the Facebook executives to participate in Senate and House hearings. This didn’t go very well the last time Zuckerberg and Co were asked to testify in Washington, and they are apparently similarly reluctant this time. According to sources none of the three that received the letter have yet responded (perhaps it got lost in the mail?) and Zuckerberg has stayed pretty quiet since he received a letter in May with questions about Libra. It does seem that David Marcus may appear at hearings about Libra on 16th and 17th July, but whether Zuckerberg and Sandberg will appear is another matter. Of course, they are easy targets in one respect. If Congress is feeling tetchy about cryptocurrency and regulations around it, then targeting Facebook, which is already a business giant is easier for them, or perhaps makes more sense to them, than pulling in Vitalik Buterin of Ethereum. But here’s the thing; just wait till Congress finds out they can’t send letters to Bitcoin!

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AspectD: An Effective AOP Solution for Flutter Open Sourced by Alibaba Xianyu Tech Team This article is part of Alibaba’s Utilizing Flutter series. Github. AspectD for Flutter Background With the rapid development of the Flutter framework, more and more businesses begin to use Flutter to refactor or build new products. However, in practice, we have found that, on the one hand, Flutter has a high development efficiency, excellent performance, and good cross-platform performance. On the other hand, Flutter also faces problems, such as missing or imperfect plug-ins, basic capabilities and the underlying framework. For example, in the process of implementing an automated recording and playback, we have found that the code of the Flutter framework (Dart level) needs to be modified to meet the requirements during automatic recording playback. This leads to the risk of the framework becoming vulnerable to intrusion. To solve this problem and reduce the maintenance cost in the iteration process, the first solution we consider is Aspect-Oriented Programming. Aspect-Oriented Programming (AOP) can dynamically insert the code into a specific method and position of the class at compile time (or runtime), thereby adding features to existing code dynamically and uniformly, without modifying the source code. But the question is, how can we implement AOP for Flutter? This article focuses on AspectD, a Dart-oriented AOP programming framework developed by the Xianyu tech team. AspectD: Dart-Oriented AOP Framework Whether the AOP capability is supported at runtime or compile-time, depends on the characteristics of the language itself. For example, in iOS, Objective C itself provides powerful runtime and dynamic features, making runtime AOP easy to use. In Android, Java can not only implement compile-time static proxies (such as AspectJ) based on bytecode modification, but also implement runtime dynamic proxies (such as Spring AOP) based on runtime enhancements. What about Dart? Firstly, the reflection support of Dart is poor. Only Introspection is supported, while Modification is not supported. Secondly, Flutter disables reflection for packet size and robustness. Therefore, we have designed and implemented an AOP solution based on compile-time modification, AspectD. Design Details Typical AOP Scenarios The following AspectD code illustrates a typical AOP application scenario: aop.dart

import 'package:example/main.dart' as app;import 'aop_impl.dart';

void main()=> app.main();

aop_impl.dart

import 'package:aspectd/aspectd.dart';

@Aspect()@pragma("vm:entry-point")class ExecuteDemo {  @pragma("vm:entry-point")  ExecuteDemo();

  @Execute("package:example/main.dart", "_MyHomePageState", "-_incrementCounter")  @pragma("vm:entry-point")  void _incrementCounter(PointCut pointcut) {    pointcut.proceed();    print('KWLM called!') ;  }} Developer-Oriented API design Design of PointCut @Call("package:app/calculator.dart","Calculator","-getCurTime") PointCut needs to be fully characterized how to add the AOP logic, for example in what way (Call/Execute), and to which library, which class (this item is empty in the case of Library Method), and which method.Data structure of PointCut: @pragma('vm:entry-point')class PointCut {  final Map<dynamic> sourceInfos;  final Object target;  final String function;  final String stubId;  final List<dynamic> positionalParams;  final Map<dynamic, dynamic> namedParams;

  @pragma('vm:entry-point')  PointCut(this.sourceInfos, this.target, this.function, this.stubId,this.positionalParams, this.namedParams);

  @pragma('vm:entry-point')  Object proceed(){    return null;  }} It contains the source code information (such as the library name, file name, and row number), method call object, function name, and parameter information. Please note the annotation here. Its core logic is Tree-Shaking. In AOT (Ahead of Time) compilation, if it cannot be called by the Main entry of the application, it will be discarded as useless code. The injection logic of the AOP code is non-invasive, so obviously it will not be called by the Main entry. Therefore, this annotation is required to tell the compiler not to discard this logic. @pragma('vm:entry-point') The proceed method here is similar to the ProceedingJoinPoint.proceed() method in AspectJ, and the original logic can be called by calling pointcut.proceed() method. The proceed method body in the original definition is empty, and its content will be dynamically generated at runtime. Design of Advice @pragma("vm:entry-point")Future<String> getCurTime(PointCut pointcut) async{  ...  return result;} The effect of here is the same as described above. The pointCut object is passed into the AOP method as a parameter, so that developers can obtain relevant information about the source code call to implement its own logic or call the original logic through pointcut.proceed(). @pragma("vm:entry-point") Design of Aspect @Aspect()@pragma("vm:entry-point")class ExecuteDemo {  @pragma("vm:entry-point")  ExecuteDemo();  ...  } The Aspect annotation can enable the AOP implementation class, such as ExecuteDemo, to be easily identified and extracted, and can also be used as a switch. That is, if we want to disable this AOP logic, just remove the @Aspect annotation. Compilation of AOP code Contain the Main Entry in the Original Project As we can see from the above, is introduced in aop.dart, which allows all code for the entire example project to be included when compiling aop.dart. import 'package:example/main.dart' as app; Compilation in Debug Mode The introduction of into aop.dart enables the content in aop_impl.dart to be compiled in Debug mode, even if it is not explicitly dependent by aop.dart import 'aop_impl.dart'; Compilation in Release Mode In AOT compilation (in Release mode), the Tree-Shaking logic makes the content in aop_impl.dart not be compiled into Dill when they are not called by the Main entry in aop. The impact can be avoided by adding . @pragma("vm:entry-point") When we use AspectD to write the AOP code, and generate intermediate products by compiling aop.dart, so that Dill contains both original project code and the AOP code, then we need to consider how to modify it. In AspectJ, modifications are implemented through operations on the Class file. In AspectD, it is implemented by operating the Dill file. Dill Operation The Dill file, also known as Dart Intermediate Language, is a concept in Dart language compilation. Either Script Snapshot or AOT compilation requires Dill as the intermediate. Structure of Dill We can use dump_kernel.dart provided by the VM package in the Dart SDK to print the internal structure of Dill. dart bin/dump_kernel.dart /Users/kylewong/Codes/AOP/aspectd/example/aop/build/app.dill /Users/kylewong/Codes/AOP/aspectd/example/aop/build/app.dill.txt Transformation of Dill Dart provides a Kernel-to-Kernel Transform method, which can transform Dill through recursive AST traversal of the Dill file Based on the AspectD annotation compiled by developers, the libraries, classes, and methods that need to be added with the specific AOP code can be extracted from the transformation part of AspectD, and then features, such as Call/Execute, can be implemented through operations on target classes during AST recursion. The following is part of a typical Transform logic: @override  MethodInvocation visitMethodInvocation(MethodInvocation methodInvocation) {    methodInvocation.transformChildren(this);    Node node = methodInvocation.interfaceTargetReference?.node;    String uniqueKeyForMethod = null;    if (node is Procedure) {      Procedure procedure = node;      Class cls = procedure.parent as Class;      String procedureImportUri = cls.reference.canonicalName.parent.name;      uniqueKeyForMethod = AspectdItemInfo.uniqueKeyForMethod(          procedureImportUri, cls.name, methodInvocation.name.name, false, null);    }    else if(node == null) {      String importUri = methodInvocation?.interfaceTargetReference?.canonicalName?.reference?.canonicalName?.nonRootTop?.name;      String clsName = methodInvocation?.interfaceTargetReference?.canonicalName?.parent?.parent?.name;      String methodName = methodInvocation?.interfaceTargetReference?.canonicalName?.name;      uniqueKeyForMethod = AspectdItemInfo.uniqueKeyForMethod(          importUri, clsName, methodName, false, null);    }    if(uniqueKeyForMethod ! = null) {      AspectdItemInfo aspectdItemInfo = _aspectdInfoMap[uniqueKeyForMethod];      if (aspectdItemInfo?.mode == AspectdMode.Call &&          ! _transformedInvocationSet.contains(methodInvocation) && AspectdUtils.checkIfSkipAOP(aspectdItemInfo, _curLibrary) == false) {        return transformInstanceMethodInvocation(            methodInvocation, aspectdItemInfo);      }    }    return methodInvocation;  } By traversing the AST object in Dill (the visitMethodInvocation function here), combined with the AspectD annotation written by developers (the and aspectdItemInfo here), we can transform the original AST object (the methodInvocation here) to change the original code logic, namely the Transform process. aspectdInfoMap Syntax Supported by AspectD Unlike the BeforeAroundAfter advances provided in AspectJ, only one unified abstraction is available in AspectD, which is, Around. In terms of whether to modify the original method internally, two types, Call and Execute, are available. The PointCut of the former is the call point, and the PointCut of the latter is the execution point. Call import 'package:aspectd/aspectd.dart';

@Aspect()@pragma("vm:entry-point")class CallDemo{  @Call("package:app/calculator.dart","Calculator","-getCurTime")  @pragma("vm:entry-point")  Future<String> getCurTime(PointCut pointcut) async{    print('Aspectd:KWLM02');    print('${pointcut.sourceInfos.toString()}');    Future<String> result = pointcut.proceed();    String test = await result;    print('Aspectd:KWLM03');    print('${test}');    return result;  }} Execute import 'package:aspectd/aspectd.dart';

@Aspect()@pragma("vm:entry-point")class ExecuteDemo{  @Execute("package:app/calculator.dart","Calculator","-getCurTime")  @pragma("vm:entry-point")  Future<String> getCurTime(PointCut pointcut) async{    print('Aspectd:KWLM12');    print('${pointcut.sourceInfos.toString()}');    Future<String> result = pointcut.proceed();    String test = await result;    print('Aspectd:KWLM13');    print('${test}');    return result;  } Inject Only Call and Execute are supported, which is obviously not enough for Flutter (Dart). On the one hand, Flutter does not allow reflection. To say the least, even if Flutter enables reflection, it is still not enough and cannot meet the needs. For a typical scenario, if class “y” in the x.dart file defines a private method “m” or a member variable “p” in the dart code to be injected, then it cannot be accessed in aop_impl.dart, not to mention obtaining multiple continuous private variable properties. On the other hand, it may not be enough to just operate the entire method. We may need to insert the processing logic in the middle of the method. To solve this problem, a syntax, Inject, is designed in AspectD. See the following example: The Flutter library contains the following gesture-related code: @override  Widget build(BuildContext context) {    final Map<TapGestureRecognizer> gestures = <Type, GestureRecognizerFactory>{};

    if (onTapDown ! = null || onTapUp ! = null || onTap ! = null || onTapCancel ! = null) {      gestures[TapGestureRecognizer] = GestureRecognizerFactoryWithHandlers<TapGestureRecognizer>(        () => TapGestureRecognizer(debugOwner: this),        (TapGestureRecognizer instance) {          instance            ..onTapDown = onTapDown            ..onTapUp = onTapUp            ..onTap = onTap            ..onTapCancel = onTapCancel;        },      );    } If we want to add a processing logic for the instance and context after onTapCancel, Call and Execute are not feasible. However, with Inject, only a few simple statements are needed to solve the problem: import 'package:aspectd/aspectd.dart';

@Aspect()@pragma("vm:entry-point")class InjectDemo{  @Inject("package:flutter/src/widgets/gesture_detector.dart","GestureDetector","-build", lineNum:452)  @pragma("vm:entry-point")  static void onTapBuild() {    Object instance; //Aspectd Ignore    Object context; //Aspectd Ignore    print(instance);    print(context);    print('Aspectd:KWLM25');  }} Through the above processing logic, the GestureDetector.build method in Dill after compilation is as follows: In addition, compared with Call/Execute, the input parameter of Inject has an additional lineNum named parameter, which can be used to specify the specific row number of the insert logic. Build Process Support Although we can compile aop.dart to compile both the original engineering code and the AspectD code into the dill file, and then implement the Dill hierarchy transformation through Transform to implement AOP, the standard Flutter build (flutter tools) does not support this process, so minor changes to the build process are still required. In AspectJ, this process is implemented by Ajc of the non-standard Java compiler. In AspectD, support for AspectD can be implemented by appending the Patch to flutter_tools. kylewong@KyleWongdeMacBook-Pro fluttermaster % git apply --3way /Users/kylewong/Codes/AOP/aspectd/0001-aspectd.patchkylewong@KyleWongdeMacBook-Pro fluttermaster % rm bin/cache/flutter_tools.stampkylewong@KyleWongdeMacBook-Pro fluttermaster % flutter doctor -vBuilding flutter tool... Practice and Consideration Based on AspectD, we have successfully removed all invasive code for the Flutter framework in practice, and implemented the same features as when the intrusive code was not removed, supporting the recording and playback of hundreds of scripts and the stable and reliable operation of automatic regression. From the perspective of AspectD, Call/Execute can help us easily implement features, such as performance tracking (call duration of key methods), log enhancement (obtaining detailed information about the place where a method is specifically called), and Doom recording and playback (such as the build record and playback of random number sequences). The Inject syntax is more powerful. It can implement the free injection of logic by means similar to the source code, and support complex scenarios, such as app recording and automatic regression (for example, recording and playback of user touch events). Furthermore, the AspectD principle is based on the Dill transformation. With the power of Dill, developers can freely operate on Dart compiled products. In addition, this transformation targets AST objects, which are close to the source code level, and are not only powerful but also reliable. Whether it is the logical replacement or the Json←> model transformation, it provides a new perspective and possibility. Conclusion As a new Flutter-oriented AOP framework developed by the Xianyu tech team, AspectD supports mainstream AOP scenarios, and is open-source on Github. AspectD for Flutter If you have any questions or suggestions during usage, please feel free to submit an or a . issue pull request (Original article by Wang Kang王康) Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

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Deep Aging: Scientists Say AI Is Getting Better at Predicting Your True Age Researchers say AI is getting better at guessing a person’s most important age — their biological age. You’ve heard that saying, “age is just a number.” It turns out that there’s a lot of truth to it. And, as the saying suggests, that chronological number many of us try to avoid mentioning on our birthdays is really just a number — and far less relevant than we assume. The real age we should focus on is our biological age, which is influenced by factors as diverse as genes, lifestyle, behavior and environment. Scientists say that this age — not the number that gets you into bars at 21, or lets you apply for Social Security at 62 — more closely predicts our lifespan and healthspan and is, therefore, a more accurate reflection of our true age. But, so far, scientists have found determining a person’s biological age to be extremely difficult, if not impossible. Now, a team of scientists report that the accurate prediction of biological age is becoming more feasible, thanks to artificial intelligence and new, vast publicly available datasets. The researchers summarized the current work on deep aging clocks in a recent issue of . Trends in Pharmacological Sciences According to Alex Zhavoronkov, Ph.D, founder and CEO of , people are good at using images, videos, voice and even smell to guess another person’s age. But, AI can guess ages even more accurately, he said in . Insilico Medicine a news release “Deep neural networks can do it better and we can now interpret what factors are most important,” said Zhavoronokov. “Very often when someone looks older than their chronological age, they are sick. A trained doctor can guess the health status of a patient just by looking at him or her. At Insilico we developed a broad range of deep biomarkers of aging that can be used by the pharmaceutical and insurance companies, as well as by the longevity biotechnology community. In this paper we describe the recent progress in this emerging field and outline a range of non-obvious applications.” The researchers suggest that there’s not just one indicator — or biomarker — that can tip off a person’s biological age, but a combination of these different predictors. AI is suited for this task because it is adept at sorting through all the available data and determining the right combination of factors. Accurately estimating biological ages won’t just be a cool trick for AI systems to perform at birthday parties, either. The researchers said that identifying the predictors of aging could help scientists better understand the aging process and reveal keys to healthy aging. Pharmaceutical and insurance companies, as well as medical and longevity industries, would all be keenly interested in that trend. “Deep biomarkers of aging developed utilizing a variety of data types of aging are rapidly advancing the longevity biotechnology industry. Using biomarkers of aging to improve human health, prevent age-associated diseases and extend healthy life span is now facilitated by the fast-growing capacity of data acquisition, and recent advances in AI. They hold a great potential for changing not only aging research, but healthcare in general,” said Polina Mamoshina, senior scientist at , in the news release. Insilico Medicine The researchers predict that AI and aging research will accelerate in the future and note that acceleration is being driven by the increasing number of people entering this field — from universities to corporations — as well as the number of funding sources adding money to those organizations.

Deep Aging: Scientists Say AI Is Getting Better at Predicting Your True Age

Instant Cryptocurrency Exchange Comparison — Changelly vs. ChangeNOW The number of crypto-related products expands every single day. As blockchain technology finds new ways of innovative uses in every sphere of our lives, new crypto services appear at the speed of light. To choose a good and reliable crypto project, you need to do your own research. To simplify the task, we made this instant crypto exchange comparison between ChangeNOW and Changelly service. Why Choose Instant Crypto Exchange Services As the number of crypto exchange hacks is growing every day, it’s not safe to keep cryptocurrency in a crypto exchange account. Ok then, where to exchange crypto? — In instant exchange services such as ChangeNOW or Changelly. What’s important these type of services are non-custodial, which practically means they don’t hold users’ savings, they just implement exchange and send crypto funds to a user wallet. In this case, another good question is what wallet to choose to store crypto? The best way to save crypto is cold storage. Changelly and ChangeNOW Numbers Compared Now, let’s take a look at the main characteristics of two crypto exchange services, namely ChangeNOW and Changelly. We collected their main features in the table below: *according to Trustpilot ChangeNOW ChangeNOW, a cryptocurrency exchange service, operates with crypto-to-crypto exchanges only and offers its users to exchange cryptocurrencies two different ways: using the floating rate or the fixed rate mechanism. Initially, ChangeNOW was built on the Changelly API. That is why the interfaces of these two services look pretty similar. However, there are several differences between them. The ChangeNOW platform has its own NOW token, that has recently moved from Ethereum blockchain to Binance mainnet. The token serves as the initial cryptocurrency when using ChangeNOW solutions. The service doesn’t require user data besides exceptional cases when a user needs to pass KYC for security reasons. Changelly To date, Changelly is one of the oldest instant crypto exchange services on the market. Since it was found in 2015, Changelly gained the trust of millions of users. According to statistics, most Changelly indicators go hand in hand with other instant exchange services. However, there are distinctive features that make Changelly stand out from the crowd. Changelly 24/7 customer support The “secret weapon” of this crypto exchange service is its customer support. It’s no secret that when exchanging crypto, different kind of issues may occur. In most cases, the reason is a human error (mishandling). For example, if a user sends crypto to the wrong wallet address, he or she will likely lose all the sent assets. However, with the help of Changelly support team, there are much more chances that cryptocurrency won’t be lost forever. Every time Changelly customers get into trouble, the customer support is always there to help them, 24 hours 7 days a week. Changelly crypto purchase solution The other major advantage is the opportunity to buy crypto with a bank card right inside the service. Not only Bitcoin. All the listed cryptocurrencies are available for crypto-purchase. The supported fiat currencies to buy crypto for are USD, EUR, and GBP. These solutions are provided in collaboration with two Changelly partners — and . Simplex Indacoin Crypto Takeaways To sum it up, let’s take a look at the comparing table once again. The numbers provided show that both services are easy in use, offer various crypto assets for the exchange, don’t require user’s documents, and provide seamless transactions within 30 minutes. However, some distinctions may seem crucial for some users, namely: Changelly has 24/7 customer support ChangeNOW has its token for internal operations Changelly gives an option to buy crypto for fiat ChangeNOW doesn’t require an email Changelly doesn’t require the wallet address for repeated transactions So, think for yourself and choose a service that suits you the best way. Good luck in your further crypto investments! Originally published at https://changelly.com on July 10, 2019.

Binance

Instant Cryptocurrency Exchange Comparison — Changelly vs. ChangeNOW

The Hedge Fundization of VC A year ago, I wrote a post about ‘ ’. Since then, a lot has been written and said about this shift in the VC landscape. The Rise of Algorithmic VC Data is starting to play a major role in VC and PE, and today I would like to give you an insider view based on my past experience working at a VC fund, and my current findings from running . ( ) Crunchdex an alternative data company that tracks over 17 million companies to help world-leading investors and corporates identify the fastest growing ones When I was launching Crunchdex 4 months ago, I already knew there was a demand for alternative data in the VC space. However, I had no idea how big of a trend it actually was. Crunchdex — CX500 The first mention of quantitative approach to VC that I was able to find online is from . Back then, only a handful of funds were actually doing something with data. Nowadays, the vast majority of bigger VC funds (i.e. >$1BN AUM) either already have a data strategy or are currently developing one. November, 2012 Now, why is that? As with any other trend, there is a bunch of underlying factors. 1. Availability of private company data Company data providers have been very busy in the past decade. Some managed to aggregate massive chunks of stale company data (e.g. Bureau van Dijk). Some were lucky enough to experience strong network effects (e.g. Angel List or Crunchbase). Some scraped the living hell out of the internet (not going to point fingers here 🤐) . And some got pretty good at estimating interesting growth signals (e.g. App Annie or SimilarWeb). 2. Significant rise in investment activity across all geographies and segments Almost every quarter, it’s the same headlines — “record amount of VC investments in Europe”; “CVC investment activity has quintupled over the past 4 years”. Record here, record there. You get the picture. However, more deployed capital = more (new) funds, and naturally = more competition. When I was working in VC, I would argue the biggest challenge wasn’t spotting good companies; the biggest challenge was getting into deals. Ask any VC. 3. VC has become a super-appealing career path I think VC has become the new investment banking. It’s shiny from the outside — it seems glamorous, it’s a hot topic, and if you are lucky enough, you get to meet and work with some of the most talented founders in the world. Nowadays, even mediocre funds receive hundreds of job applications for junior roles. The best funds receive thousands. And all these young, fresh-out-of-school VCs want to make a mark. Given their network is usually very limited, company databases become a natural starting point. 4. The rise and popularity of quant hedge funds If you check articles about hedge funds from 2014–2016, you will find a significant amount of headlines about hedge funds hiring quants. Quant hedge funds (like Two Sigma, D.E. Shaw or Renaissance) have been around for a long time now. If you check Google Trends, you can see one massive spike in 2008. And then again, a small spike around 2016. 5. If the best ones are doing it, everyone else will follow — CB Insights The Top 100 Venture Capitalists This doesn’t apply only to VC, it applies to any industry. I can tell you with 100% confidence that 9 out of 9 the most successful VCs have already developed a strong data strategy. How do I know? We already supply data to some of them, and we have been in touch with 7/9 of them in the past few months. 6. Tech Namely; API integrations, Big Data tools, and database tools. Not convinced? Here are a few more supporting stats 1. Funds are hiring a lot of data people Source: Alternativedata.org 2. Funds are spending a lot more on alternative data Source: Alternativedata.org 3. The rise of alternative data providers Source: Alternativedata.org What is the current status of the VC industry? I am basing this part on our actual pipeline = 100+ funds that we actually spoke to. That doesn’t mean that one can draw conclusions about the entire industry, but I would claim it’s a pretty good proxy. 1. Most bigger US funds (>$1BN AUM) are ‘data-ready’ and know exactly which data points they need and want 🇺🇸🏅 🇪🇺🐢 2. European funds are lagging behind That was perhaps the biggest finding from the past 4 months. I am really surprised how many big European VC funds do nothing meaningful with data compared to their US counterparts. 🧰 3. Many VCs are in what I call the ‘BUY or BUILD’ stage This is obviously a very biased view, due to the fact that the analysis is based on our own pipeline. However, again — if you check job openings across VC funds, you will be able to see quite a few data science positions. 📈 4. Many new funds implement data right from the start Why? Because LPs frequently ask about it during fundraising. And again, data is a good starting point for deal flow — not only for new VCs, but also for new funds. 😢 5. Unfortunately, many VCs are still not ‘data-ready’ Data has not been part of the core DNA of most VC funds that have been around for a while. Some of them have unsuccessfully tried to experiment with it in the past, which has left a bitter taste in their mouth. Some just want to stick to their classic ‘GMAIL+CALENDAR routine’. And some just don’t believe that data is the right path for them. So where is the industry headed? Source: Nanex I always like to use the comparison to high frequency trading. Literally everything in our lives is becoming faster. From social interactions to food deliveries or payments — you name it, I guarantee it’s becoming faster. VC and PE will be no different. Speed will play a major role. How do you get faster? Through information = through data. And I am not saying this just because I run an alternative data company. Look at the world around us, even the dinosaurs like banks and business registers are becoming increasingly open. Most already (at least) offer some API integrations. So the amount of data about private companies will very likely increase tenfold in the next decade. By that, I am not saying we will see high frequency trading for private companies in the next 10 years, simply because of issues linked to equity and liquidity (actually, on second thought, who knows). But I am almost certain that this industry will go through a massive shift towards quant. The same shift hedge funds underwent some time ago. Speed will really matter. How fast can you spot new, up and coming companies and how fast can you react to growth signals about growth stage companies will be key to success. Of course things like brand, network and experience will still matter. However, especially for very early stage opportunities — speed will be the #1 parameter of success. Which opens a window of opportunity for many. And I personally cannot wait to see this happen…

Your retro action items aren’t working. Here’s why. A retrospective is a time for our team to look back. Its function, however, is to improve the way in which we move forward. Let’s digest from Scrum: the original definition The Sprint Retrospective is an opportunity for the Scrum Team to inspect itself and create a plan for improvements to be enacted during the next Sprint. The first two components are activities, the third is a result. And while most teams have fruitful conversations and create action items to yield that result, few have systems in place to ensure that positive change is actually created. Inspect ✔ Create a plan ✔ Enact improvements 🤷‍♀️ The problem starts with the notion of “improvement.” It’s both, vague and subjective, so even a plan of activities feels like a step in the right direction (Spoiler: it’s not). If you take measures to concretely define improvement, however, you can hold yourself and your team accountable to your action items. For that, we can use SMART goals. SMART goals contextualize improvement Research has shown that goals that are both and are considerably more likely to yield results than generic action items. specific time-bound Putting a number and a date to each retrospective action item ensures: The department understands and aligns on what constitutes success, and Progress towards this goal is black and white — trending toward or away from the goal. While there are plenty of systems that put a number and date to goals, but for the sake of this post, we’ll stick to one that’s tried-and-true: SMART ( pecific, easurable, chievable, elevant, ime-bound) goal-setting. S M A R T To best position your team to work with SMART goals, you’ll need to adjust all three components of the retro. You’ll be with more data, using SMART goals, and by making progress transparent to everyone on the team. inspecting creating a plan enacting improvements Inspect: Use data to diagnose the biggest issues Most teams decide on goals using only qualitative feedback. A team member raises what they perceived to be a large bottleneck, and the whole team immediately starts trying to mitigate that issue. This method gives precedence to what individuals remember and feel, not necessarily the largest and most pressing problems. If you bring more data points into diagnosing the problem, however, you’re more likely to get a holistic understanding of each bottleneck. Quantitative data helps counteract recency bias and enables you to prioritize based on actual risk that the problems present to your team’s productivity. Let’s say a given engineering team is trying to diagnose why they didn’t get to as many features as they anticipated this sprint. One engineer, Hannah, makes the following hypothesis: I feel like there were more pull requests than usual that were open at any one given time. I think it’s because people were too busy to get to code reviews, so work piled up. Several engineers nod their head. They also noticed that there were more open PRs than usual in GitHub. Instead of immediately brainstorming action items, Hannah and her team investigate further. They start by looking at their Time to Review this past sprint, and realize it’s relatively low — just 6 hours. This is contradictory to Hannah’s assessment that the review process was slower than usual. From there, they see that their average number of Review Cycles is about 1.2, where most Pull Requests are approved after one review. Also, seems pretty good. Finally, they found a red flag when they looked at their Time to Merge. They realize that many pull requests stay open for a long time after they’re reviewed as developers move on to new tracks of work. The gut instinct of the team recognized the symptom — long-running pull requests — but not the cause. Without data, they couldn’t have uncovered and addressed a deeper systemic problem. Other data points you may consider looking at: including Pull Requests, Code Reviews, and Tickets, to remind your team of what they worked on last sprint, and where they might have gotten stuck. All recent activities, Activity Log represents every engineering activity with a shape. Hover over to get context on what a team member is working on. Velocity’s last sprint. Look at pull requests that had a big effect on the codebase, as well as pull requests that were larger or older than the rest. The most important pull requests shows work in progress with activity level, age, and health. See at-a-glance the pull requests that are most likely to impede your team. Velocity including outcome metrics like Cycle Time and Pull Request Throughput, but also metrics that represent more specific areas of the software development process, like Time to Open, Time to Review, and Time to Merge. Process metrics lets you visualize the journey of your pull requests from open to merged. Below, you can see metrics that represent constituents of this journey to better diagnose slowdowns. Velocity Plan: Align with SMART goals Once your team has fully diagnosed an issue using both qualitative and quantitative data, they’ll have to decide on one, specific metric that they can use as their SMART goal. Specific The success of hitting or missing your metric should be black or white, so you need a concrete number in your goal. “Improving our Time to Review” is vague, “Decreasing our Time to Review to under 4 hours” is specific. Also, make sure the metric is narrow enough that the team knows which behaviors drive this metric up or down. Metrics that are too broad can obscure progress since they’re affected by many different kinds of unrelated data. Hannah’s team, for example, would want to choose a metric like Time to Review, rather than total Cycle Time, so the team can easily self-correct when they notice the metric trending in a negative direction. M easurable The way in which you measure your metric depends on your objective. If you’re measuring output, for example, a simple count can do the trick. If you’re looking to adhere to specific standards — such as keeping pull requests small, or keeping downtime minimal — you’ll want to decide between tracking the simple average and tracking it as a sort of Service Level Objective (SLO) based on a percentile. Here are a few examples: While averages are more commonly used in process metrics, SLOs enable your team to deviate from the goal in a few instances without hindering their ability to meet the target. Assignable Pick one person to own and track this goal. Research has shown that having exactly one team member check in at regular intervals drastically increases the chances that a goal will be hit. Apple championed the idea of a for all initiatives, and teams at leading tech companies like Microsoft have applied the Directly Responsible Individual (DRI) DRI model to all DevOps related functions. Ownership will also help you secure buy-in for bringing data into retros. Consider asking the person who uncovered the problem in the first place to own the goal. Realistic Make sure your goal is reachable, so your team feels success if they’ve put a concerted effort into reaching the goal. Execute: Increase visibility to keep goals front of mind The true test of your action items come after the retro. How frequently will your team think about these metrics? Will success be known across the team? If your team is unsuccessful, will they be able to try a different adjustment? To keep the goal front of mind, you need to make progress visible to everyone on the team. Many managers use information radars, either in shared spaces or in universally accessible dashboards. provides a Target dashboard that lets you visualize progress towards your SMART goals. Velocity Making progress transparent equips the team to bring the results-oriented discussions outside of their retros. Effective goals will be brought up during standups, 1:1s, and even pairing sessions. Repetition will secure focus and will further unify the team around success. 📈 makes engineers 😊 When Boaz Katz, the founder and CTO of Bizzabo, he found that sharing success motivated his team to find more ways to improve. He told us, “My team developed a winning attitude and were eager to ship even faster.” started setting concrete targets When the whole team feels success each retro, the momentum creates a flywheel effect. Team members are eager to uncover more improvement opportunities creating a culture around enacting positive change to your processes.

Microsoft

Velocity

Your retro action items aren’t working. Here’s why.

CasperLabs Announces Node 0.5, Gets Ready for Public DevNet Bonding transaction in Rust PoS CBC-Casper Blockchain Protocol Stays on Release Schedule Following June Launch of Node 0.4. , builders of the fully decentralized and scalable next-generation blockchain implementing pure CBC-Casper Proof of Stake (PoS) protocol, has announced the release of . With this release, it is now possible to add nodes to the network and participate in consensus by sending a bonding request. New York, July 11, 2019 — CasperLabs Node 0.5 CasperLabs’ Node 0.5 includes all of the necessary features to support a public DevNet with the introduction of validator bonding, unbonding, and the Proof of Stake contract. Together, they enable one of the defining characteristics of CBC-Casper: participants in consensus can join and leave the network without going through any proxy voting process. Also known as being “permissionless,” this feature is an essential property of high security blockchains. The CasperLabs blockchain supports smart contracts that compile to WASM. Presently, compilation targets for Rust are available. Medha Parlikar, CEO at CasperLabs, shared, “I am thrilled to announce the release of Node 0.5 which paves the way to our public DevNet release next week.” Parlikar added, “Our incredible development team continues to make solid progress towards the public release of our network later this year.” Highlights of the CasperLabs Node 0.5 release include: — Deployments (transactions) must now be signed using the public key associated with the account performing the deployment. Transactions being executed from a given account must employ a sequential nonce, to prevent replay attacks and to ensure deterministic transaction execution for a given account. Sending Deployments The CasperLabs blockchain uses accounts, which is where tokens are stored, within purses. Accounts are only created in conjunction with a token transfer request and are secured with an ed25519 compatible key pair. When an account is created, a purse is created in the mint, tokens are transferred to the new purse, and a new account object is written to the global state under the passed PublicKey. The account also includes additional fields for thresholds, associated_keys, and account_activity, which will be important for account recovery, a feature planned for a future release. Accounts and Purses — As deploys are received from an account, the account public key is used to access the execution pre-state context from global state. The contract is executed, the execution effects are applied to the pre-state to compute the post-state and the post-state is stored in global state under the account’s public key. This post-state will now be the “pre-state” for the next deploy for the account. — CBC-Casper Proof of Stake enables validators to join and leave the network by sending a request to the network in the form of a special transaction known as a “bonding or unbonding request. Validator Bonding and Unbonding The CBC-Casper protocol is provably safe. This means that a mathematical proof exists for the algorithm demonstrating that transactions, once finalized by CBC-Casper, cannot be reversed. The Casper protocol achieves this by using a special to measure the level of finality achieved for a given block. Once a transaction has been submitted, it is possible to use the GraphQL interface located at port 40403 to obtain finalization information. Finalizing Transactions — algorithm Additional technical details and coding examples, as well as links to the GitHub developer package and documentation, can be found in the CasperLabs’ . official announcement CasperLabs Team About CasperLabs CasperLabs is building a transformative blockchain for all applications that require a high degree of security, fast execution, and predictable economics. The company’s technology is built on the purest implementation of the proof-of-stake CBC-Casper protocol, designed by CasperLabs lead architect and Ethereum researcher Vlad Zamfir. CasperLabs delivers a fully-decentralized, permissionless, public, and open source blockchain platform that supports developers with robust tools, application insights, and predictable fee structures. Website: https://casperlabs.io/ Telegram: https://t.me/casperlabs LinkedIn: https://www.linkedin.com/company/casperlabs/ GitHub: https://github.com/CasperLabs

CasperLabs

CasperLabs Announces Node 0.5, Gets Ready for Public DevNet

Why (and when) you should use Kubernetes Kubernetes is a powerful container management tool that automates the deployment and management of containers. Kubernetes (k8’s) is the next big wave in cloud computing and it’s easy to see why as businesses migrate their infrastructure and architecture to reflect a cloud-native, data-driven era. Getting started with Kubernetes? Try out the Practical Guide to Kubernetes and start running production grade clusters. Outlined in this post are some of the top reasons why you should use Kubernetes and when you should/shouldn’t use it. Container orchestration Containers are great. They provide you with an easy way to package and deploy services, allow for process isolation, immutability, efficient resource utilization, and are lightweight in creation. But when it comes to actually running containers in production, you can end up with dozens, even thousands of containers over time. These containers need to be deployed, managed, and connected and updated; if you were to do this manually, you’d need an entire team dedicated to this. It’s not enough to run containers; you need to be able to: Integrate and orchestrate these modular parts Scale up and scale down based on the demand Make them fault tolerant Provide communication across a cluster You might ask: aren’t containers supposed to do all that? The answer is that containers are only a low-level piece of the puzzle. The real benefits are obtained with tools that sit on top of containers — like Kubernetes. These tools are today known as container schedulers. Great for multi-cloud adoption With many of today’s businesses gearing towards microservice architecture, it’s no surprise that containers and the tools used to manage them have become so popular. Microservice architecture makes it easy to split your application into smaller components with containers that can then be run on different cloud environments, giving you the option to choose the best host for your needs. What’s great about Kubernetes is that it’s built to be used anywhere so you can deploy to public/private/hybrid clouds, enabling you to reach users where they’re at, with greater availability and security. You can see how Kubernetes can help you avoid potential hazards with “vendor lock-in”. Deploy and update applications at scale for faster time-to-market Kubernetes allows teams to keep pace with the requirements of modern software development. Without Kubernetes, large teams would have to manually script their own deployment workflows. Containers, combined with an orchestration tool, provide management of machines and services for you — improving the reliability of your application while reducing the amount of time and resources spent on DevOps. Kubernetes has some great features that allow you to deploy applications faster with scalability in mind: Horizontal infrastructure scaling: New servers can be added or removed easily. Auto-scaling: Automatically change the number of running containers, based on CPU utilization or other application-provided metrics. Manual scaling: Manually scale the number of running containers through a command or the interface. Replication controller: The replication controller makes sure your cluster has an equal amount of pods running. If there are too many pods, the replication controller terminates the extra pods. If there are too few, it starts more pods. Health checks and self-healing: Kubernetes can check the health of nodes and containers ensuring your application doesn’t run into any failures. Kubernetes also offers self-healing and auto-replacement so you don’t need to worry about if a container or pod fails. Traffic routing and load balancing: Traffic routing sends requests to the appropriate containers. Kubernetes also comes with built-in load balancers so you can balance resources in order to respond to outages or periods of high traffic. Automated rollouts and rollbacks: Kubernetes handles rollouts for new versions or updates without downtime while monitoring the containers’ health. In case the rollout doesn’t go well, it automatically rolls back. Canary Deployments: Canary deployments enable you to test the new deployment in production in parallel with the previous version. “Before Kubernetes, our infrastructure was so antiquated it was taking us more than six months to deploy a new microservice. Today, a new microservice takes less than five days to deploy. And we’re working on getting it to an hour.” — Box Better management of your applications Containers allow applications to be broken down into smaller parts which can then be managed through an orchestration tool like Kubernetes. This makes it easy to manage codebases and test specific inputs and outputs. As mentioned earlier, Kubernetes has built-in features like self-healing and automated rollouts/rollbacks, effectively managing the containers for you. To go even further, Kubernetes allows for declarative expressions of the desired state as opposed to an execution of a deployment script, meaning that a scheduler can monitor a cluster and perform actions whenever the actual state does not match the desired. You can think of schedulers as operators who are continually monitoring the system and fixing discrepancies between the desired and actual state. Overview/additional benefits You can use it to deploy your services, to roll out new releases without downtime, and to scale (or de-scale) those services. It is portable. It can run on a public or private cloud. It can run on-premise or in a hybrid environment. You can move a Kubernetes cluster from one hosting vendor to another without changing (almost) any of the deployment and management processes. Kubernetes can be easily extended to serve nearly any needs. You can choose which modules you’ll use, and you can develop additional features yourself and plug them in. Kubernetes will decide where to run something and how to maintain the state you specify. Kubernetes can place replicas of service on the most appropriate server, restart them when needed, replicate them, and scale them. Self-healing is a feature included in its design from the start. On the other hand, self-adaptation is coming soon as well. Zero-downtime deployments, fault tolerance, high availability, scaling, scheduling, and self-healing add significant value in Kubernetes. You can use it to mount volumes for stateful applications. It allows you to store confidential information as secrets. You can use it to validate the health of your services. It can load balance requests and monitor resources. It provides service discovery and easy access to logs. When you should use it **If your application uses a microservice architecture**If you have transitioned or are looking to transition to a microservice architecture then Kubernetes will suit you well because it’s likely you’re already using software like Docker to containerize your application. **If you’re suffering from slow development and deployment**If you’re unable to meet customer demands due to slow development time, then Kubernetes might help. Rather than a team of developers spending their time wrapping their heads around the development and deployment lifecycle, Kubernetes (along with Docker) can effectively manage it for you so the team can spend their time on more meaningful work that gets products out the door. “Our internal teams have less of a need to focus on manual capacity provisioning and more time to focus on delivering features for Spotify.”—Spotify **Lower infrastructure costs**Kubernetes uses an efficient resource management model at the container, pod, and cluster level, helping you lower cloud infrastructure costs by ensuring your clusters always have available resources for running applications. When you shouldn’t use it **Simple, lightweight applications**If your application makes use of a monolithic architecture it may be tough to see the real benefits of containers and a tool used to orchestrate them. That’s because the very nature of a monolithic architecture is to have every piece of the application intertwined — from IO to the data processing to rendering, whereas containers are used to separate your application into individual components. **Culture doesn’t reflect the changes ahead**Kubernetes notoriously has a steep learning curve, meaning you’ll be spending a good amount of time educating teams and addressing the challenges of a new solution, etc. If you don’t have a team that’s willing to experiment and take risks then it’s probably not the choice for you. What’s next? Overall, Kubernetes boasts some pretty great features that can have a positive impact on your developing/DevOps teams and for the business as a whole. If you’re looking to get started with Kubernetes, you can check out written by Viktor Farcic, a Developer Advocate at CloudBees, a member of the Google Developer Experts and Docker Captains groups, and a published author. A Practical Guide to Kubernetes ,

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Nominated for 2022 - HackerNoon Contributor of the Year - Engineering

Nominated for 2022 - HackerNoon Contributor of the Year - Engineering Management

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Why (and when) you should use Kubernetes

The shortest distance from idea to execution with Jim Rose of CircleCI Episode 55 of the : An interview with , CEO of , platform for software teams which allows to rapidly build quality projects, at scale. Hacker Noon Podcast Jim Rose CircleCI This episode of Hacker Noon is sponsored by DigitalOcean. Discover why developers love DigitalOcean and get started with a free $50 credit at https://do.co/hackernoon Listen to the interview on or watch on . iTunes YouTube In this episode Trent Lapinski interviews from . You get to discover the insights of continuous delivery and automation processes and learn what it takes to automate your apps in the cloud. Jim Rose CircleCI “Our model has always been about developer empowerment. We’ve always wanted developers to use our platform irrespective of whether they’re going to pay us, because we think, in a very community-oriented fashion, that a rising tide raises all the ships. The more developers can get familiar with more advanced technologies and more advanced approaches it’s just good for everyone, including their consumers”. — Jim Rose Production and music by Derek Bernard — www.haberdasherband.com/production Host: Trent Lapinski — https://trentlapinski.com https://hackernoon.com/ https://community.hackernoon.com/ https://contribute.hackernoon.com/ https://sponsor.hackernoon.com/ https://podcast.hackernoon.com/ https://twitter.com/hackernoon/ https://facebook.com/hackernoon/ P.S. If you dig the new Hacker Noon Podcast , consider giving us a 5 star review on iTunes. Also check out the , , and . top stories from July the latest stories today’s homepage

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The shortest distance from idea to execution with Jim Rose of CircleCI

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