Matterlessis in the Augmented Reality Industry

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THE RECORD OF CURRUPIRA

Given the recent 13% rise in Ethereum - many altcoins have entered the "relief rally" and are up 20-40%. Now I will name three reasons why such a rise is possible: 1. 🧐Correlation between the price of the altcoin and the price of the underlying asset The beta in this case is how much your asset will move relative to the underlying pair (usually ETH). For most DeFi altcoins, a -30% drop in ETH will cause the token to decline by about 50-70% - that's your "Beta coefficient". Conversely - so in this ETH rally, up ~13% from the lows, altcoins are up about 20-40% from their lows. As a starting point, you need a stable and growing underlying asset to make the beta work in your favor. 💰Conclusion #1: Expect the underlying asset to rise In challenging market conditions, your shitcoin is unlikely to rise against the decline of ETH. 2.💡 Unique fundamental events in projects. As the ETH decline stabilizes, it's important to keep an eye on the project's Twitter and Discord. As soon as you see cool fundamental news - powerful partnerships, infusions of investment, new uses for the token - that's a great reason to enter the trade. Keep in mind that cause #1 is more powerful than cause #2, so if ETH starts to fall, catalytic events at the token level matter less. But at the same time, if you are in the trend of rising ETH + the project is innovating, it will allow the project to be stronger than the market in the rebound. 💰Conclusion #2: follow the news and use it wisely. 3.⚙️ Token issuance. If large token unlocks or high farming rewards occur, your token may be losing ground to ETH (or other underlying assets), especially during the bear market. In the bull market, token issuance, high FDV and unlocks matter less 💰Conclusion #3: keep an eye on token issuance, study project tokenomics and investor funds unlocking calendar in advance. 🤔How to put this into practice? 🔥Constructively understand where the market is moving to be able to jump on the rebound wave. Monitor media sentiment, the S&P 500 and BTC chart, and the Fear Index - all of which together will help identify the trend. 🔥Figure it out - is the altcoin you're trading growing or dying? To do this - examine the project's discord and look for upcoming fundamental events, changes in tokenomics. It's worth investigating whether an event that will happen is capable of immediately changing the order of things, or whether it's a distant prospect. Most "catalysts" aren't really necessary for anyone (all those Twitter partnerships that small projects are so famous for). Look for news that will either increase the volume of demand for the token, increase TVL over time, or bring more attention to the project. Taking into account that the information asymmetry doesn't allow you to see the prospects at once - you need to buy tokens gradually, by buying up, guessing the bottom is not easy, you need to keep the position sizes small. 🔥Remember - this is not a trend movement, but only a short-term rebound, Pump. Try using leverage. This is the RISK/REWARD when you should be doing this - you can clearly see the previous bottom and can be guided by it when setting a stop loss. As a true-degen, I haven't used Binance Futures for a long time, but use various DeFi protocols like WOWswap to add leverage to my position. In addition to independence, DEXs also give you a number of advantages in an effort to drag clients from large CEXs - so, on WOWswap for example, leverage fees are charged only if the position is closed in the plus. On other platforms, you may be rewarded with native tokens for opening a position, and so on. Take advantage of these benefits. Every cent counts for trading. 💼Conclusions: Because of the small bounce, there is no need to go into FOMO and stock up on all altcoins at once. Instead, be patient, improve your research process, and find the projects and wallets that interest you the most. Be patient, don't panic. The market is going through a small rally/short-squeeze, many ALTs are still overvalued, teams are dumping, and communities are abandoning projects. Find your edge in the market, lurk, and spot-shoot underperforming projects. Disclaimer: Nothing in this article constitutes professional investment advice. Please do your own thorough research before making any investment decisions.

How to Make Money on "Relief" During the Bear Market

Erin Simpson, associate director for technology policy at Washington, D.C.–based think tank The Center for American Progress, had a message for Facebook. Last year, she co-wrote a pair of reports advising major social media platforms, like Facebook, on how to better address the twin crises of and . election delegitimization health misinformation But Facebook has also been pushing a message at her. For more than a year, Simpson had been noticing ads pop up with the same phrasing: Facebook “supports updated internet regulations,” the ads always said. The ads appeared in podcasts and on . A few months ago, when she went to the grocery store to make a copy of a key, Simpson was startled to see that even the key copying machine was flashing the message. TV "These ads follow me everywhere,” Simpson said. “How is it here? I’m at Safeway and it’s on the key copying machine. It’s taunting me.” For years, Congress has debated how to best regulate the tech giants that hold an increasing amount of control over human communication. But no comprehensive legislation dictating their behavior has been passed in a generation. According to a slew of ads like the one Simpson saw, along with public statements, blog posts, op-eds, and congressional testimony from CEO Mark Zuckerberg, Facebook would very much like to change that. The company, which spent on advertising last year, has maintained a consistent public message: Facebook is not the one slowing down internet regulation in this country, and the company would certainly welcome reasonable curbs on its power. $2.26 billion Key copying enthusiasts and members of Congress aren’t the only targets for this message. Facebook has peppered its own platform with a PR campaign aimed at users. The Markup has seen the ads turn up repeatedly on the Facebook feeds of our panel, a group of some 1,800 users that automatically share their news feed data with us. Citizen Browser Of the 1,000 most common ads appearing on our panelists’ feeds between Nov.1, 2020, and Aug 27 of this year, just one ad was from Facebook itself: “It’s been 25 years since comprehensive internet regulations were passed,” the text of the ad says. “It’s time for an update.” Targeting information collected by Citizen Browser showed the ads were aimed at users interested in “politics” and, where additional ideological targeting was available, largely to “people in the USA who are likely to engage with liberal political content.” But why is Facebook pushing for “regulations” that would curb its own power, and what exactly are the regulations it supports? The ads are not explicit, but proposals and pieces of proposals laid out on Facebook’s , which is linked in the Facebook ads, as well as in and congressional from Facebook CEO Mark Zuckerberg, offer some insight into what Facebook wants. regulations hub op-eds testimony Facebook also provided directly when The Markup reached out for information. additional materials In short, Facebook’s proposals seem to consist largely of implementing requirements for content moderation systems that Facebook has previously put in place, potentially forcing competitors to do work Facebook has already done. While there are instances, in some cases, of Facebook asking for guidance on how certain thorny questions should be handled, most of Facebook’s proposals seem unlikely to fundamentally change how the company itself does business. For skeptics about Facebook’s efforts, like Nathalie Maréchal, who does work on platform accountability at the New America Foundation’s project, the biggest problem with Facebook’s proposals are the things it doesn’t mention—the nonpolitical advertising that is the core of Facebook’s business. Ranking Digital Rights “The bigger picture here is that as long as everyone is focused on user content and all of its discontents, we are not talking about advertising. We are not talking about the money,” Maréchal explained. “It’s a PR blitz to get lawmakers and other policy-adjacent people to believe that Facebook is not standing in the way of change.” Why Is Facebook Saying It Wants Regulation? Surveys show Americans are broadly supportive of imposing new regulations on social media platforms. For example, a recent Morning Consult found that more than three-quarters of Democrats and more than half of Republicans would back legislation holding platforms accountable for spreading COVID-19 misinformation. poll Lawmakers around the country have taken notice of that sentiment. There were introduced this year regarding digital privacy. 31 state-level bills Lawmakers in have introduced bills this year that would penalize social media platforms for taking down political content, most of which were introduced after former president Donald Trump was suspended from the major social media platforms for provoking violence in the wake of the 2020 election. 18 states There’s also been action at the federal level, with legislation like Sens. Amy Klobuchar and Ben Luján’s or Sens. Brian Schatz and John Thune’s . Misinformation Act Platform Accountability and Consumer Transparency (PACT) Act Last year, the House Antitrust subcommittee issued a calling out Facebook for anti-competitive practices, and the Biden administration has pursued into the company’s practices. blistering report ongoing investigations Regulation, it seems, is in the air. Facebook, experts say, has a strong incentive to get out in front of it while also steering the conversation about new rules to the areas where it's comfortable seeing tighter government controls and away from areas where it isn’t. “Facebook is just reading the tea leaves,” Simpson said. “And they’re trying to put themselves in a position where they think they would have some credibility in the legislative conversation by saying that they’re interested in policy—not that they’re reflexively against it.” When The Markup reached out to Facebook asking for details on its regulatory proposals, spokesperson Rachel Holland replied with a statement highlighting how Facebook developed its moderation processes. “Facebook regularly works with external stakeholders when developing our Community Standards to balance competing interests, such as freedom of expression, safety, and privacy,” Holland said. “Depending on the topic, we will continue to work with subject matter experts, civil society organizations, and law enforcement, among others, to develop robust content policies that keep communities safe and apply to a global user base.” Facebook isn’t the first technology company to publicly push for greater regulation. Microsoft, for example, has been a of tighter regulations on the use of facial recognition technology. major supporter A law passed in the company’s home state of Washington last year regulating facial recognition technology was sponsored by a lawmaker who for Microsoft. literally worked Facebook spent nearly lobbying the U.S. government last year, making it the of any organization. However, Sen. Mark Warner, who has introduced about regulating big tech platforms, said Facebook hasn’t been doing a lot of the things savvy corporations do when they want their favored legislation passed. $20 million sixth-highest spender several bills “There’s no doubt that Facebook, like other industry giants, knows exactly what it takes to get a bill passed and signed into law. Unfortunately, despite a pervasive ad campaign designed to tout its support for updated internet regulations, Facebook’s proactive engagement has been limited and it has continued to sponsor a number of groups focused on undermining regulatory proposals,” Warner told The Markup. “As we continue to try to bring internet law into the 21st century, I invite Facebook to engage meaningfully with my office on this complex issue.” Facebook’s campaign is more than political; it’s public relations, said Matt Navarra, a U.K.-based social media consultant who produces a newsletter and podcast about social platforms. Facebook’s decision to go directly to the public with its legislative message, in addition to its lobbying, he said, is part of a larger shift in brand marketing, largely enabled by social platforms like Facebook. “It wasn't so many decades ago that if a company had a big issue it was trying to influence, it would just stick to the usual routes of lobbying ministers and making their products seem super amazing,” Navarra explained. “Now it’s easy to build a community around a cause and activate people. We've seen a shift in how people communicate and engage with each other because of those platforms, and that's led to a shift in why companies want to reach consumers with these messages. What Exactly Is Facebook Proposing? Facebook’s ad campaign centers on the Communications Decency Act. The 1996 law has a provision, , which makes websites generally posted by users and provides the legal basis platforms use to justify their content moderation policies. Section 230 not liable for the content Section 230 is widely considered the guiding legal principle for the modern internet; since many people across the political spectrum have qualms about the modern internet, Section 230 has become something of a flashpoint. Facebook’s ads, which often at least obliquely reference Section 230, wisely don’t mention many of the specific reasons people might be upset enough with Facebook to desire new rules governing its conduct. Instead, the ads focus on the law’s age. The quarter-century since its passage is an eternity in internet years. In before Congress in March, Zuckerberg proposed that the law should be updated to require tech platforms to have certain content moderation policies and systems in place to catch “unlawful content and activity on their platforms.” testimony If those systems exist, platforms can continue to enjoy all the protections—meaning essentially zero liability for anything that’s posted on their platforms—of Section 230. He went on to ask Congress to pass legislation to “bring more transparency, accountability, and oversight to the processes by which companies enforce their rules about content that is harmful but legal.” While Zuckerberg’s testimony doesn’t go into detail, proposals in Facebook’s public-facing policy documents, conveniently, mostly happen to be things that Facebook already put in place following public pressure. Some are things already required of the company by laws like the European Union’s General Data Protection Regulation or the California Consumer Privacy Act ( ). GDPR CCPA These processes, outlined , include: here Allowing users to report problem content and having a content moderation program in place—both of which Facebook . already does Having some form of external body evaluate content moderation standards and also provide oversight on enforcement decisions. Facebook already has an . oversight board Publishing content moderation standards, which Facebook after those standards were to The Guardian. began doing leaked Providing transparency into how content moderation standards are set, which Facebook has done since 2018 by publishing the of the internal meetings where its content policy is set (although those minutes were eventually replaced by glossy, less informative slide presentations). minutes Giving users input on content rules, which Facebook at least ostensibly does through its process. Stakeholder Engagement Allowing affected users to appeal content moderation decisions, a process Facebook in 2018. implemented Requiring responses to all user-initiated content reports, which Facebook users can on the platform. track the status of Notifying users when their content is removed, which Facebook . does Disclosing publicly aggregate content enforcement numbers, which Facebook has been doing for years, with companies’ potentially getting hit with more oversight or even sanctions if the prevalence of offending content on their platform hits a certain threshold. quarterly The documents in Facebook's regulation hub also spell out a handful of things Facebook want to see in new rules. doesn’t The company says it is requiring platform moderators to pre-approve user content before it can be posted, short time frames for taking down content, the criminalization of misinformation, and any kind of specific product design mandates. against Facebook also says that, ideally, any content moderation requirements should scale up with size, giving smaller platforms more leeway than larger ones. Facebook singled out existing proposals for regulating political ads to combat foreign interference—the and the —as ones it supports, although, as Quartz , Facebook simultaneously claimed to back the Honest Ads Act in public while lobbying against its passage in private. DETER Act Honest Ads Act reported The company favors passing federal privacy legislation modeled very closely on privacy rules already on the books in Europe. “I believe it would be good for the internet if more countries adopted regulation such as GDPR as a common framework,” Zuckerberg in a 2019 Washington Post op-ed. wrote In that same op-ed, he supported requiring platforms to have data portability, which is the idea that, as Zuckerberg wrote, “if you share data with one service, you should be able to move it to another.” An element of data portability is mandated as part of the GDPR and the CCPA, and in the op-ed, Zuckerberg highlighted Facebook’s support for the , an open-source initiative creating a common framework for sharing user data among online service providers. Data Transfer Project Facebook has a for users to port things like their photos and posts but doesn’t currently offer the ability to move users’ friends lists to other services, as as a way to more meaningfully increase competition. tool some have called for In addition to requiring some measure of data portability, Facebook it hopes the government provides guidance on some of the privacy issues surrounding moving data across platforms, particularly when moving one person’s data also involves transferring information about other users. said For example, what happens if someone ports their friends list from a social network where people can go by pseudonyms to one where everyone uses their real name? That could expose people's real identities without their consent. Daphne Keller, director of the Program on Platform Regulation at Stanford’s Center for Internet and Society, said this desire for clarity is likely born out of regulatory inconsistencies surrounding data portability under E.U. law that the company wouldn’t want to see replicated under U.S. law. “They wanted people to stop asking them for incompatible things, for someone with authority to tell them what the real answer is,” Keller said. “They don’t care what it is. They just want someone to decide so they're not in an impossible position.” Could Passing Facebook’s Proposals Actually Be Harmful? Offering already implemented policy changes, said Maréchal of Ranking Digital Rights, lets Facebook crowd out tougher legislation, like a proposed bill that would require transparency in . targeted digital advertising The bill, which has , popped up in May in the wake of Facebook’s to NYU researchers studying ad targeting on the platform. not yet come up for a vote cutting off access Many of Facebook’s now favored policies, Maréchal noted, were pushed by public interest groups over the course of a decade before Facebook finally implemented them. “They're conceding on a point that they've already lost,” she said. And new regulations might very well hurt competitors more than they hurt Facebook. Even just dragging GDPR across the pond from Europe, as Facebook suggests, would trigger significant compliance costs from U.S. companies. While major players with big European footprints, like and , have GDPR compliance all squared away, Keller notes that many other U.S. firms would almost certainly have to invest significant resources to survive an investigation by European regulators. Facebook Google Scaling these requirements up as companies grow, as Facebook suggests, is a common feature in this type of legislation. However, Aaron Mackey, a senior staff attorney at the Electronic Frontier Foundation who has about Facebook’s proposals, worries that setting up a regulatory framework where the end goal is to be like Facebook directly encourages an internet where everything works like Facebook. written critically “Not every large social media service desires or aspires to be like Facebook or even aspires to monetize their users in a certain way. That’s a problem too, this idea that the law should be modeled off of one individual company and the way they have developed their business model,” Mackey said. “There are a lot of for-profit or community-led or individual-led user generated services and social networks that don't have that model and they don't aspire to be like Facebook.” Facebook’s proposals haven’t seemed to gain much traction on Capitol Hill. After Zuckerberg put forward these ideas in congressional testimony earlier this year, Politico lawmakers on both sides of the aisle who were deeply skeptical of his intentions. talked to “Mark Zuckerberg knows that rolling back section 230 will cement Facebook's position as the dominant social media company and make it vastly harder for new startups to challenge his cash cow,” Sen. Ron Wyden, a Democrat who was an original author of Section 230, told Politico. “Section 230 reform will hit Facebook regardless of what these self-interested Silicon Valley CEOs want,” concurred Republican senator Marsha Blackburn, also to Politico. “Big Tech only wants reform when it bolsters their power at the expense of competitors.” Additional reporting by Corin Faife This article was originally published on The Markup and was republished under the Creative Commons Attribution-NonCommercial-NoDerivatives license. Written by By: Aaron Sankin.

How Much Internet Regulation Does Facebook Support? 

Product Bugs are one of the most common and accepted parts of building tech products. Yet bugs are often overlooked or an afterthought, especially when it comes to thinking about how to improve productivity and, ultimately, get better outcomes for customers. Product bugs don’t get much love because they aren’t sexy and are mostly associated with trouble, or in the more extreme form, disasters. They can be anything from mildly annoying to the cause of a or . $1 billion rocket being destroyed $440m being lost in 30 minutes Bugs can even be a taboo topic. Some people want to believe that they won’t happen or don’t want to talk about them so that they don’t risk being seen as bad at their job. No one intends to introduce bugs into products – I’ve never seen a roadmap naming the bugs a team wants to introduce – but product bugs must be planned for because they’re inevitable. They’re so common that that “software bugs, or errors, are so prevalent and so detrimental that they cost the US economy an estimated $59 billion annually or about 0.6% of the gross domestic product.” the US Department of Commerce concluded Macro statistics aside, you’ve been impacted by bugs personally if you’ve spent less than 15 minutes building or using tech. This article is in the form of a guide, that aims to comprehensively cover everything you, your team and stakeholders need to think about when it comes to product bugs (software defects, errors, software bugs, or whatever you want to call them). What Are Product Bugs? Wikipedia as “an error, flaw or fault in a computer program or system that causes it to produce an incorrect or unexpected result, or to behave in unintended ways.” defines software bugs For our purposes, we’re going to use the same definition for product bugs. Most tech products are heavily software or technology-based. “Most bugs arise from mistakes and errors made in either a program’s design or its source code, or in components and operating systems used by such programs.” For products that have physical aspects or hardware, you can extend this to include errors or mistakes in the product’s physical design or production. Why Do Product Bugs Occur? A simple explanation for why product bugs occur comes from the definition above: and usually by the people that built the product, but sometimes by the user. mistakes errors made However, it is a bit more complicated than that. The Institute of Electrical and Electronics Engineers, the , is the body responsible for standards in software. The IEEE has a which provides the following categories for causes of bugs: IEEE Standard Classification for Software Anomalies The paper found that the majority of defects came from the Development process, with data and integrations coming in next. A Case Study: Software Defect Root Causes Within the development process, there are a variety of ways bugs can occur, including: , especially when changes are made later in the development cycle. Changes to requirements like omissions, conflicting requirements and logical mistakes. Issues with the requirements themselves Failing to incorporate testing (manual and automated) into the development process. Testing, but testing too late in the development process. Avoidable mistakes from stressed and disenfranchised software engineers. Bugs from integrations, anecdotally, primarily come from (mis)behaviour of another software system, undocumented functionality and unexpected changes in the other system. Errors resulting from data processing problems are only going to rise due to the increase in the volume and uses of data. Usability bugs are an interesting category in that often they are reported on or improved outside of thinking about bugs. For example, say a signup process isn’t performing as well as you think; it could because there are too many steps or screens. Usually, this isn’t reported as a bug but gets thought of as an improvement, unless the signup wasn’t built to the original signup specifications. We won’t resolve this issue here, instead, just make sure you are aware of it. Usability issues also include accessibility issues (e.g. WCAG 2.0 compliance), problems to do with platforms or browsers (e.g. Apple v Android, Chrome v Safari). Why Do Product Bugs Both Matter and Not Matter? Product bugs matter because they are guaranteed to occur and they are guaranteed to have an impact on your product. Bugs could lose your customers, cause financial losses or, worse, injury or death (depending on your industry). Bugs can also lead to personal stress and angry stakeholders. However, if bugs are accepted, respected and managed, then they don’t need to matter. Somewhat counterintuitively, by allowing for bugs to both happen, be allowed for in your software architecture and be part of your development lifecycle, then bugs matter less (or at least feel less painful). If you’re running trying to rapidly understand, validate and grow a customer base, then your allowance for bugs becomes a necessity. Facebook is famous for and truly lived the phrase “move fast, break stuff”. You might not allow for as many bugs as Facebook did due to your customers, but equally, you might want or need to in order to get ahead in the market (Facebook has ). since rolled this back a bit You need to work out what allowance you will make for bugs depending on your circumstances. Zero bugs are usually not the right answer too. You also need to work out , and to know whether bugs in plural or if a single bug matter. what your priorities are to get this setting right Next Steps In the second part of this guide, you’ll learn how to manage product bugs in-depth. Previously published at https://productcoalition.com/product-bugs-a-comprehensive-guide-part-1-e6532b28260a

The Ultimate Guide to Product Bugs:  Part 1

Creativity matters, the tool doesn’t! UX designers defend design tools the same way a fanboi defends tech companies and their products. The company doesn’t care if you support one or multiple products. Design tools matter less than what you think. I came across some designers who asked me which tool I use for designing and I was greeted with this same response from more than half of them: "Why aren’t you using XYZ tool? It’s so good than ABC tool in terms of [some common features]. You should definitely switch!" Why? Unless the tool limits you from either creating something, doesn’t support your device, or genuinely lacks a feature which hinders your design pace, you should be good to use pretty much any design tool which is commonly used. When it finally struck me I thought of making an App UX design using 4 design tools. Technically, each of them aren’t design tools. Framer & Invision serve the purpose of a prototyping tool primarily but still can be used for designing the prototypes. About Don’t forget that these are tools are developed and maintained by big companies which don’t care about your opinion when adding, removing, or changing a feature in their product. This means nothing is certain and, if you stick to only one tool, you might limit yourself to only the features which that tool offers. Also, using a design tool isn’t a skill. It’s just good practice to display them in your portfolio to depict how strong you’re with the tool. My Approach https://www.linkedin.com/posts/sunitshirke_prototype-uidesign-uxdesign-activity-6697178995760361472-XBBc The UX which I have attached above is from my Linkedin post where I had posted the initial release of the UX design. Below are the links of all the design tools in which I have recreated the same UX. So, as it will be quite visible that design tools don’t matter if you have to create a complete UI/UX for an App/Website. NOTE: I have excluded Sketch and other design tools because I haven’t used them personally so if I use them in future I’ll update the article. Figma: https://www.figma.com/proto/0jotWkvhsEaXswDlcFQxvw/AppTest?node-id=3%3A2&viewport=336%2C193%2C0.4479730725288391&scaling=scale-down Adobe Xd: https://xd.adobe.com/view/1a8a7198-2891-4b5b-ac25-c0adbed7479c-4fd9/?fullscreen Framer: https://framer.com/share/AppTest--3oXvGdUL3WiWadumKcff Invision: https://sunitshirke399831.invisionapp.com/console/share/HU1NFT328E/483961260 How do the “Giants” do it? Companies, or rather design teams, can pretty much do anything from sticking to a particular tool to using many tools in their design process. They have a lot of things to factor into creating a great design like dev handoff, presentation features, access sharing, team collaboration, pricing and much more. Conclusion Stop depending on tools to be creative. Always try everything and then choose a tool according to your use case or whatever suits you best. Follow design principles. Adapt to new design tools. Keep on building your design knowledge from various sources. The only tool which I don’t change for whiteboarding is which kind of serves the purpose of UX in a lighter way. It’s not a prototyping tool but definitely worth trying. Excalidraw PS: You can follow companies like Zomato , Uber , Google for a good understanding of design principles. Also published at https://medium.com/design-bootcamp/heres-why-design-tools-don-t-matter-df7c20f55e96

Your Creativity is More Valuable Than The Tools You Use

AI and HR seem to be a match made in heaven — in some innovative heaven, driven by smart technologies. Intelligent apps and chatbots have penetrated deep into the processes associated with managing human resources. They are here to meekly do all boring repetitive tasks like screening resumes, answering common questions or onboarding new employees. Can we expect more from digital hard workers? Of course. The new generation of is supposed to ensure a personalized approach to each employee, forecast the workforce behavior, and help HR managers with detecting, motivating, and retaining talent. AI technologies Will a particular candidate be a good match for the position? What should be done to increase performance across the company? Who is going to leave your company in the near future? Who is ready to become a team leader? Smart digital assistants can potentially generate intelligent data-driven answers to these and other complex questions. In this article, we’ll review the most advanced use cases of artificial intelligence in human resources management and discuss some of AI’s limitations. Hiring the right employees AI has already gone far in hiring people. The achievements of conversational chatbots like are really striking: they can answer up to of the questions from candidates while of interviewed people do not even realize that they are interacting with a bot, not with a human agent. Mya 75% 73% When it comes to choosing candidates with the right qualifications, technologies have also proved to be extra helpful. The point is, our decisions are often driven by emotions, and not always for the better. As one survey revealed, one in five UK bosses decides whether or not to hire an applicant within of a job interview. Sixty seconds are hardly enough time to objectively evaluate a person’s skills and qualities. Employers judge prospective employees on first impressions, based on appearance, clothing, tone of voice or even a type of a handshake. None of these parameters are relevant to education, problem-solving ability or professional experience (with a few exceptions such as hiring models for the beauty industry). one minute AI algorithms are not affected by emotions, so they can painstakingly sift through dozens or even hundreds of applicants to detect qualified candidates. For instance, the recruiter’s AI assistant helps to find the right person faster and reduces cost per hire by leveraging big data and predictive analytics. AI-fueled solutions like or not only match people to a job but also eliminate gender, racial and other biases. Hiredscore Pymetrics PredictiveHire Yet AI still can’t fully replace a human in the hiring process. Many studies show that technical skills often matter less than soft skills. Sociability, curiosity, and critical thinking are as important as tech expertise for of executives. Another survey reports that when they are hiring, organizations with the highest financial return pay just as much attention to a candidate’s , such as the ability to learn quickly, a sense of purpose and ambition. All of these features are much harder or even impossible to evaluate using existing math algorithms and pure logic. 92% psychological traits To sufficiently uncover soft skills and emotional characteristics, AI requires self-awareness, which implies reflecting on one’s own thoughts. Most AI researchers believe that machines will learn to ‘know themselves’ in the way people do somewhere between . Until that time, we won’t be able to do without live recruiters when performing final candidate assessment. 2036 and 2060 Just-in-time learning and professional development Millennials, or people born between 1981 and 1996, are already dominating the job market, and by 2030, they will comprise over of the US workforce. of them underscore the importance of career growth and professional development. And what about the present day? The 2018 Workplace Learning Report that people strive for more flexible and faster upskilling, with 68% of workers preferring to learn at work and 58% opting for learning at their own pace. two thirds 87% shows To attract and retain talent, the most farseeing organizations already offer AI-powered learning opportunities to their staff. Most modern platforms for staff development exploit algorithms similar to those used by Netflix, Spotify, and other content-streaming services. They aggregate data about employees’ preferences, background, and skills to provide articles, courses, videos, books, and other resources which fit their needs best. AI-fueled solutions like , , or help businesses match workers with relevant learning content, design personalized development strategies, and build skills that really matter both for the company and employees. Moreover, with smart technologies, the learning process becomes as engrossing as watching ‘Game of Thrones’ (or whichever TV series you like best). EdCast Degreed BetterUp Axonify Identifying areas of stress and preventing toxic behaviors Another promising usage of AI is to control fraud and non-compliance. Intelligent software can filter email traffic, and perform sentiment analysis of comments made by employees. In such a way, it can recognize patterns indicating ethical lapses, workplace bullying, loss of motivation, or fraudulent behavior. This information will allow HR managers to address risks before problems really occur. One of the predictive tools already existing in the market is . To detect risk areas, it analyses emails, chats in messaging systems, and other digitally written texts created across the organization over the past two years. Then the system starts aggregating and evaluating real-time conversational patterns. As a result, managers gain insights into what is actually happening in the organization at the personal level, whether there are any areas of tension and conflict, and more. KeenCorp Finding hidden stars and rising leaders, and managing talents If AI is able to identify possible fraudsters, can’t it find high-potential leaders, hidden stars, and influencers inside the organization as well? The fact is it can, or at least is already taking steps in this direction. There are hundreds of psychological studies that list the personality traits of effective leaders. Here, the AI mission is to help HR managers spot desired characteristics and behavioral patterns in existing employees. Again, an organization’s real-time communication flows become a source of valuable information. By measuring interactions, AI solutions like can answer the following questions: TrustSphere How do employees collaborate with each other as well as with clients and suppliers? Who influences whom in your company? Who acts as a leader? Which collaboration network is the most productive? What small changes in the behavior would lead to better performance? HR managers can use the results of internal communication audits to improve employee engagement, align team members with organizational goals, give ambitious people more opportunities to shine, retain talent and predict employee leave. The opportunities are endless, though a human perspective is needed to explore all of them. The story of success to be continued We must admit that: AI is not omnipotent. It lacks the intuition and imagination of a person. Its decisions are based on pure logic while people are often irrational, and HR managers shouldn’t ignore this fact. Even the smartest chatbot is not ready to take the place of a human being in circumstances such as conducting a final job interview or preventing a vital employee from leaving their job. What AI-fueled tools can really do best is to aggregate data, recognize and match patterns, and make data-driven predictions. Algorithms are able to analyze millions of pieces of information in seconds. Yet it’s still up to a human expert to evaluate the results and make the final decision. In order to stay competitive under current market conditions, AI is inevitable. In the future, AI systems will become even more clever, reliable and specialized. The story of cooperation between artificial intelligence and human resources is only beginning, and we’re sure that new breakthroughs are just around the corner. <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

The Predictive Power and Cognitive Limitations of AI in HR

Special thanks to Eli Ben-Sasson for ongoing help and explanations, and Justin Drake for reviewing In the last part of this series, we talked about how you can make some pretty interesting succinct proofs of computation, such as proving that you have computed the millionth Fibonacci number, using a technique involving polynomial composition and division. However, it rested on one critical ingredient: the ability to prove that at least the great majority of a given large set of points are on the same low-degree polynomial. This problem, called “low-degree testing”, is perhaps the single most complex part of the protocol. We’ll start off by once again re-stating the problem. Suppose that you have a set of points, and you claim that they are all on the same polynomial, with degree less than D (ie. deg < 2 means they’re on the same line, deg < 3 means they’re on the same line or parabola, etc). You want to create a succinct probabilistic proof that this is actually true. tooltip Left: points all on the same deg < 3 polynomial. Right: points not on the same deg < 3 polynomial If you want to verify that the points are on the same degree < D polynomial, you would have to actually check every point, as if you fail to check even one point there is always some chance that that point will not be on the polynomial even if all the others are. But what you do is that at least (eg. 90%) of all the points are on the same polynomial. all can probabilistically check some fraction tooltip tooltip Top: possibly close enough to a polynomial. Bottom: not close enough to a polynomial. Bottom left: somewhat close to two polynomials, but not close enough to either one. Bottom right: definitely not close enough to a polynomial. If you have the ability to look at point on the polynomial, then the problem is easy. But what if you can only look at a few points - that is, you can ask for whatever specific point you want, and the prover is obligated to give you the data for that point as part of the protocol, but the total number of queries is limited? Then the question becomes, how many points do you need to check to be able to tell with some given degree of certainty? every Clearly, D points is enough. D points are exactly what you need to uniquely define a degree < D polynomial, so set of points that you receive will correspond to degree < D polynomial. As we see in the figure above, however, D+1 points or more give some indication. not any some do The algorithm to check if a given set of values is on the same degree < D polynomial with D+1 queries is not too complex. First, select a random subset of D points, and use something like Lagrange interpolation (search for “Lagrange interpolation” for a more detailed description) to recover the unique degree < D polynomial that passes through all of them. Then, randomly sample one more point, and check that it is on the same polynomial. here tooltip Note that this is only a proximity test, because there’s always the possibility that most points are on the same low-degree polynomial, but a few are not, and the D+1 sample missed those points entirely. However, we can derive the result that if less than 90% of the points are on the same degree < D polynomial, then the test will fail with high probability. Specifically, if you make D+k queries, and if at least some portion p of the points are not on the same polynomial as the rest of the points, then the test will only pass with probability (1 - p)k. But what if, as in the examples from the previous article, D is very high, and you want to verify a polynomial’s degree with less than D queries? This is, of course, impossible to do directly, because of the simple argument made above (namely, that k <= D points are all on at least one degree < D polynomial). However, it’s quite possible to do this indirectly , and achieve massive efficiency gains by doing so. And this is exactly what new protocols like (“Fast RS IOPP”, RS = “ ”, IOPP = “Interactive Oracle Proofs of Proximity”), and similar earlier designs called probabilistically checkable proofs of proximity (PCPPs), try to achieve. any by providing auxiliary data FRI Reed-Solomon A First Look at Sublinearity To prove that this is at all possible, we’ll start off with a relatively simple protocol, with fairly poor tradeoffs, but that still achieves the goal of sublinear verification complexity - that is, you can prove proximity to a degree < D polynomial with less than D queries (and, for that matter, less than O(D) computation to verify the proof). The idea is as follows. Suppose there are N points (we’ll say N = 1 billion), and they are all on a degree < 1,000,000 polynomial f(x). We find a bivariate polynomial (ie. an expression like 1 + x + xy + x^5*y^3 + x^12 + x*y^11), which we will denote g(x, y), such that g(x, x^1000) = f(x). This can be done as follows: for the kth degree term in f(x) (eg. 1744 * x^185423), we decompose it into x^(k % 1000) * y^floor(k / 1000) (in this case, 1744 * x^423 * y^185). You can see that if y = x^1000, then 1744 * x^423 * y^185 equals 1744 * x^185423. In the first stage of the proof, the prover commits to (ie. makes a Merkle tree of) the evaluation of g(x, y) over the square [1..N] x {x^1000: 1 <= x <= N} - that is, all 1 billion x coordinates for the columns, and all 1 billion corresponding for the y coordinates of the rows. The diagonal of the square represents the values of g(x, y) that are of the form g(x, x^1000), and thus correspond to values of f(x). entire thousandth powers The verifier then randomly picks perhaps a few dozen rows and columns (possibly using if we want a non-interactive proof), and for each row or column that it picks the verifier asks for a sample of, say, 1010 points on the row and column, making sure in each case that one of the points demanded is on the diagonal. The prover must reply back with those points, along with Merkle branches proving that they are part of the original data committed to by the prover. The verifier checks that the Merkle branches match up, and that the points that the prover provides actually do correspond to a degree-1000 polynomial. the Merkle root of the square as a source of pseudorandomness tooltip This gives the verifier a statistical proof that (i) most rows are populated mostly by points on degree < 1000 polynomials, (ii) most columns are populated mostly by points on degree < 1000 polynomials, and (iii) the diagonal line is mostly on these polynomials. This thus convinces the verifier that most points on the diagonal actually do correspond to a degree < 1,000,000 polynomial. If we pick thirty rows and thirty columns, the verifier needs to access a total of 1010 points * 60 rows+cols = 60600 points, less than the original 1,000,000, but not by that much. As far as computation time goes, interpolating the degree < 1000 polynomials will have its own overhead, though since polynomial interpolation can be made subquadratic the algorithm as a whole is still sublinear to verify. The complexity is higher: the prover needs to calculate and commit to the entire N * N rectangle, which is a total of 1018 computational effort (actually a bit more because polynomial evaluation is still superlinear). In all of these algorithms, it will be the case that proving a computation is substantially more complex than just running it; but as we will see the overhead does not have to be high. prover that A Modular Math Interlude Before we go into our more complex protocols, we will need to take a bit of a digression into the world of modular arithmetic. Usually, when we work with algebraic expressions and polynomials, we are working with regular numbers, and the arithmetic, using the operators +, -, *, / (and exponentiation, which is just repeated multiplication), is done in the usual way that we have all been taught since school: 2 + 2 = 4, 72 / 5 = 14.4, 1001 * 1001 = 1002001, etc. However, what mathematicians have realized is that these ways of defining addition, multiplication, subtraction and division are not the self-consistent ways of defining those operators. only The simplest example of an alternate way to define these operators is modular arithmetic, defined as follows. The % operator means “take the remainder of”: 15 % 7 = 1, 53 % 10 = 3, etc (note that the answer is always non-negative, so for example -1 % 10 = 9). For any specific prime number p, we can redefine: The above rules are all self-consistent. For example, if p = 7, then: 5 + 3 = 1 (as 8 % 7 = 1)1 - 3 = 5 (as -2 % 7 = 5)2 * 5 = 33 / 5 = 2 (as (3 * 55) % 7 = 9375 % 7 = 2) More complex identities such as the distributive law also hold: (2 + 4) * 3 and 2 * 3 + 4 * 3 both evaluate to 4. Even formulas like (a2 - b2) = (a - b) * (a + b) are still true in this new kind of arithmetic. Division is the hardest part; we can’t use regular division because we want the values to always remain integers, and regular division often gives non-integer results (as in the case of 3 / 5). The funny p-2 exponent in the division formula above is a consequence of getting around this problem using , which states that for any nonzero x < p, it holds that xp-1 % p = 1. This implies that xp-2 gives a number which, if multiplied by x one more time, gives 1, and so we can say that xp-2 (which is an integer) equals 1 / x. A somewhat more complicated but faster way to evaluate this modular division operator is the , implemented in python . Fermat’s little theorem extended Euclidean algorithm here tooltip Because of how the numbers "wrap around", modular arithmetic is sometimes called "clock math" With modular math we’ve created an entirely new system of arithmetic, and because it’s self-consistent in all the same ways traditional arithmetic is self-consistent we can talk about all of the same kinds of structures over this field, including polynomials, that we talk about in “regular math”. Cryptographers love working in modular math (or, more generally, “finite fields”) because there is a bound on the size of a number that can arise as a result of any modular math calculation - no matter what you do, the values will not “escape” the set {0, 1, 2 ... p-1}. Fermat’s little theorem also has another interesting consequence. If p-1 is a multiple of some number k, then the function x -> xk has a small “image” - that is, the function can only give (p-1)/k + 1possible results. For example, x -> x2 with p=17 has only 9 possible results. tooltip With higher exponents the results are more striking: for example, x -> x8 with p=17 has only 3 possible results. And of course, x -> x16 with p=17 has only 2 possible results: for 0 it returns 0, and for everything else it returns 1. Now A Bit More Efficiency Let us now move on to a slightly more complicated version of the protocol, which has the modest goal of reducing the prover complexity from 1018 to 1015, and then 109. First, instead of operating over regular numbers, we are going to be checking proximity to polynomials . As we saw in the previous article, we need to do this to prevent numbers in our STARKs from growing to 200,000 digits anyway. Here, however, we are going to use the “small image” property of certain modular exponentiations as a side effect to make our protocols far more efficient. as evaluated with modular math Specifically, we will work with p = 1,000,005,001. We pick this modulus because (i) it’s greater than 1 billion, and we need it to be at least 1 billion so we can check 1 billion points, (ii) it’s prime, and (iii) p-1 is an even multiple of 1000. The exponentiation x1000 will have an image of size 1,000,006 - that is, the exponentiation can only give 1,000,006 possible results. This means that the “diagonal” (x, x1000) now becomes a diagonal with a wraparound; as x1000 can only take on 1,000,006 possible values, we only need 1,000,006 rows. And so, the full evaluation of g(x, x1000) now has only ~1015 elements. tooltip As it turns out, we can go further: we can have the prover only commit to the evaluation of g on a single column. The key trick is that the original data itself already contains 1000 points that are on any given row, so we can simply sample those, derive the degree < 1000 polynomial that they are on, and then check that the corresponding point on the column is on the same polynomial. We then check that the column itself is a < 1000 polynomial. tooltip The verifier complexity is still sublinear, but the prover complexity has now decreased to 109, making it linear in the number of queries (though it’s still superlinear in practice because of polynomial evaluation overhead). And Even More Efficiency The prover complexity is now basically as low as it can be. But we can still knock the verifier complexity down further, from quadratic to logarithmic. And the way we do is by making the algorithm recursive. We start off with the last protocol above, but instead of trying to embed a polynomial into a 2D polynomial where the degrees in x and y are equal, we embed the polynomial into a 2D polynomial where the degree bound in x is a small constant value; for simplicity, we can even say this must be 2. That is, we express f(x) = g(x, x^2), so that the row check always requires only checking 3 points on each row that we sample (2 from the diagonal plus one from the column). that If the original polynomial has degree < n, then the rows have degree < 2 (ie. the rows are straight lines), and the column has degree < n/2. Hence, what we now have is a linear-time process for converting a problem of proving proximity to a polynomial of degree < n into a problem of proving proximity to a polynomial of degree < n/2. Furthermore, the number of points that need to be committed to, and thus the prover’s computational complexity, goes down by a factor of 2 each time (Eli Ben-Sasson likes to compare this aspect of FRI to , with the key difference that unlike with FFTs, each step of recursion only introduces one new sub-problem instead of branching out into two). Hence, we can simply keep using the protocol on the column created in the previous round of the protocol, until the column becomes so small that we can simply check it directly; the total complexity is something like n + n/2 + n/4 + … ~= 2n. fast fourier transforms tooltip In reality, the protocol will need to be repeated several times, because there is still a significant probability that an attacker will cheat round of the protocol. However, even still the proofs are not too large; the verification complexity is logarithmic in the degree, though it goes up to log2(n) if you count the size of the Merkle proofs. one The “real” FRI protocol also has some other modifications; for example, it uses a binary (another weird kind of finite field; basically, the same thing as the 12th degree extension fields I talk about , but with the prime modulus being 2). The exponent used for the row is also typically 4 and not 2. These modifications increase efficiency and make the system friendlier to building STARKs on top of it. However, these modifications are not essential to understanding how the algorithm works, and if you really wanted to, you could definitely make STARKs with the simple modular math-based FRI described here too. Galois field here Soundness I will warn that - that is, determining just how low the probability is that an optimally generated fake proof will pass the test for a given number of checks - is still somewhat of a “here be dragons” area in this space. For the simple test where you take 1,000,000 + k points, there is a simple lower bound: if a given dataset has the property that, for any polynomial, at least portion p of the dataset is not on the polynomial, then a test on that dataset will pass with at most (1-p)k probability. However, even that is a very pessimistic lower bound - for example, it’s not possible to be much more than 50% close to low-degree polynomials at the same time, and the probability that the first points you select will be the one with the most points on it is quite low. For full-blown FRI, there are also complexities involving various specific kinds of attacks. calculating soundness two is a recent article by Ben-Sasson et al describing soundness properties of FRI in the context of the entire STARK scheme. In general, the “good news” is that it seems likely that in order to pass the D(x) * Z(x) = C(P(x)) check on the STARK, the D(x) values for an invalid solution would need to be “worst case” in a certain sense - they would need to be maximally far from valid polynomial. This implies that we don’t need to check for much proximity. There are proven lower bounds, but these bounds would imply that an actual STARK need to be ~1-3 megabytes in size; conjectured but not proven stronger bounds reduce the required number of checks by a factor of 4. Here any that The third part of this series will deal with the last major part of the challenge in building STARKs: how we actually construct constraint checking polynomials so that we can prove statements about arbitrary computation, and not just a few Fibonacci numbers. All content written by me is by default released freely under the . WTFPL

STARKs, Part II: Thank Goodness It's FRI-day

One of the most common arguments against Bitcoin is that it is not backed by anything. That it’s price is just speculation. But what does it mean for an asset or a currency to be backed by something in the first place? To find this out, we first need to defy what it means for something to have value. After all, if something is to be backed by something else, that something else better be something of value. Like gold. Why does gold have value? Is it valuable because you can make jewelry out of it or is jewelry made out of it because it’s valuable? You can’t eat it and it’s not very good for any other basic human need. So what makes it valuable? A fundamental thing that people miss when thinking about value is that value is subjective. Not a single thing is of the same value to one person as it is to another whether the price is the same or not. A price means one thing to a wealthy person and another to a poor person since the amount of time and effort they have to sacrifice in order to acquire that thing differs between the two. Things like debt and compound interest also play a huge role here. In addition to this, the value of something priced in any fiat currency changes over time since the value of the currency is constantly being diluted through inflation. The value of something boils down to two distinct variables, supply and demand. Supply is objective to the buyer and demand is subjective. This is also true for the medium of exchange or type of money that is being used to carry out the transaction. Money is simply the language we use to express how we subjectively value things to each other. always always always So what does “being backed” by something mean? Basically it’s a guarantee that the token being used will be redeemable for some other thing that have proven to keep its value over time, at a later point in time. Currencies around the world used to be redeemable for a certain amount of gold but this isn’t true for any fiat currency at this point. It has not been true for the world’s current reserve currency, the U.S. Dollar, since 1971 when president Nixon decided to unpeg the dollar from gold. Ever since that day, the value of the dollar has been backed by nothing but the good word of of the Federal Reserve Bank and the U.S. government whose current president is Donald Trump. Gold has shown to be a good store of value for around 5000 years. It is valuable because it is hard to produce more of it, period. When the price of an asset goes up, producing more of it becomes more profitable. This in turn leads more of the asset being produced, increasing the supply hence lowering the price again until an equilibrium is reached. In the case of gold, the amount of it already extracted from the ground is high in comparison to that which is still yet to be found. This means that an increased gold price does not lead to any significant increase of the total supply of gold available for sale despite the increased production efforts. It is this high stock-to-flow ratio of gold that has led it to such a good store of value over time. The demand for gold is ever changing but its supply stays relatively scarce. Bitcoin is scarcity. Even though you can’t make jewelry or anything else out of Bitcoin, its total supply is . After the year 2140, after the last Bitcoin has been mined, the total amount of Bitcoins in circulation can only go . This limited supply is what the gold standards of the past were there for in the first place. Bitcoins are a lot more limited though as they will be lost as time goes by. Since the supply is so limited it what the current demand is. The potential upside to its value is literally limitless due to this relationship between supply and demand. The “backing” that other currencies have is only there to ensure people that the currency will keep its value over time and the only way of ensuring this is to limit the supply. Bitcoin does this better than any other thing before it. Leonardo da Vinci’s original paintings are extremely valuable since there’s only about 13 of them left. One day there’ll be Bitcoin left. absolute fixed down doesn’t matter less than one Bitcoin doesn’t need to be backed by anything. On the contrary, the best thing anything can be backed by Bitcoin, since Bitcoins are absolutely scarce and completely programmable at the same time. is

“But, it’s not backed by anything?”

It has been said that democracy is the worst form of government except all others that have been tried. But what about those that been tried? Is there even room for criticism of democracy itself in a democracy? Regardless of the answer, there’s no way of opting out of a democracy. You’re born into it, just like you’re born into any other system of civilization that’s currently running as the operative system of your nation. Should you happen to be born in the United States of America today, you would be born into a system that has elected a somewhat unusual leader. A leader that has been described as a poor man’s version of a rich man, a weak man’s version of a strong man and a stupid man’s version of a wise man. Many of those who describe him as such are still baffled by the fact that he got elected in a democratic election. What most fail to see is that he probably got elected of the aforementioned traits rather than despite them. A poor man’s version of a rich man, a weak man’s version of a strong man and a stupid man’s version of a wise man is the perfect candidate in a democratic system because in a true democracy, poor, weak and stupid people have the same right to vote as everyone else. haven’t because Wherever you happen to be born and whatever your opinion about the system you happen to be born into, your options are always limited by whatever rules and regulations that happen to govern that particular geographic area at that particular time. Rules that very rarely have anything to do with whether a particular action is moral or not. All systems of governance that humanity has tried so far, including democracy, haven’t favoured truly moral behaviour in populations but rather behaviour that is well adapted to the very specific set of rules that they happen to be born into. This is all too often reflected in the jaw-droppingly un-sophisticated behaviour of our so called leaders. Behaviour found not only in the White House, but across the globe and across the political spectrum. One can’t help but thinking that the leaders of the world shouldn’t struggle with language or maths but few, if any, seem to master both. To be unfit for office seems to be a prerequisite for those that want to hold it. It’s a catch 22 and an unfortunate side effect of collaborations between human brains in general. So what can you do about it? Sadly, not a lot on the macro scale. Your vote won’t matter. It’s that simple. What you do is to ignore the cyclical nature of the specific system you happen to be born in and shift your attention towards things that actually do have an impact on your life and the lives of those around you. The internet is a dangerous place for the gullible but a fantastic resource for the curious mind. You can never opt out of the whole system but you can choose to ignore more and more aspects of it as the internet evolves. First of all you can stop listening to whatever propaganda-machines that are running on your nations media networks. You can cover your house in solar panels. Get off as many grids as you can. Last but not least you can start using Bitcoin, the only sound type of money this world has to offer. The amount of people you can transact with without hassle might still be very small, but usage is growing at a staggering rate. Transacting is also not the only way in which you can use it. One of the most mind-blowing features of Bitcoin is that every participant of the network has the power to increase its price by simply using it and thereby limiting its supply. Hodling, as this behaviour is often referred to as, is using. The bigger the movement grows, the more useful it becomes and the more powerful a tool it becomes. It liberates the little man from the economic constraints of whatever governance-scheme he has fallen victim to. The earlier you get on this train, the more rewarded your courage will be and the more liberated you’ll become. can not Your political opinions, let alone your opinions on democracy as a whole, will matter less and less in the coming decades. Free-thinkers, anarcho-capitalists and libertarians all around the globe have this new tool to shield themselves with from the whims of third parties. We will, finally, get to witness the power of a truly free market on a global scale. The transition period might be short or long, peaceful or violent, spectacular or almost invisible but make no mistake, change coming. Pandora’s box has been opened and if the beast we found inside turns out to be Roko’s basilisk or not remains to be seen. Regardless, you should probably prepare yourself. Democracy’s days are counted. is

The problem with democracy

Blockchain Dilbert Cartoon by Scott Adams The future of blockchain lies in the development of digital identity, web 3.0 projects and in compelling solutions to critical problems like scalability, brutal latency, computational power, limited storage and interoperability. That sounds like a lot of crypto buzz words. And, yes, it is to common people. The huge valuation boom that engulfed the crypto market last year is still continuing its bull run in the ICO market despite the cryptocurrency bear market. In 2018 alone, the collective funds raised in ICO is close to $6B but how much of that has translated into token adoption? How many token adopters are using the token product or platform for personal or business use cases? Have they organically shifted their loyalty/usage from the legacy systems to the crypto platform or product? Is there a void in the token adoption market? (The legal issues hindering mass adoption of cryptocurrencies and tokens are beyond the scope of this article.) The Chicken & Egg Problem Earlier, in 2016, crypto experts called out that the token adoption will solve the for startups, that is, The answer to that was thought to be exploiting “Network Effects — chicken and egg problem how to build a base of customers/believers in spite of having no product but just an innovative world changing idea? A (also called or ) is the positive effect described in economics and business that an additional user of a good or service has on the value of that product to others. When a network effect is present, the value of a product or service increases according to the number of others using it”. network effect network externality demand-side economies of scale It was presented that token adoption can be built through network value if early believers can be converted into early participants turned investors in the idea business. Below is well known graph on the chicken and egg problem solution through Network Effect Model. Vinny Lingham Courtesy Vinny Lingham Civic Tokens used for raising funds during an ICO have reached an adoption cycle crisis. Many retail investors who wanted to make quick money from sudden cryptocurrency price rise had pumped money into the ICO market in 2017. But these retail investors have rarely adopted the crypto products for use. As of now more than 90% of successful and unsuccessful ICO tokens have landed in deadcoins.com . Top crypto startups have only around 7–8k token adopters, which is actually a very sad number. A few years ago, if your startup had 7–8k users, then you know you have traction problem. Right now the every crypto startup is facing this crisis. . So how did we end up here where the token adoption rate is so low that it is actually hurting the future of mass blockchain adoption Ignoring the crypto scams and suddenly rich founders who abandoned their crypto startups after quick gain; many of the legitimate blockchain startups with great products are striving hard to create an ecosystem for successful token adoption. We have heard a lot about network effects but currently these tokens need the magic of consumer adoption effects. So what could be the possible reasons? Courtesy Quoine Why the Network Effect did not solve the Token Adoption Problem. There is a recent article by MIT Sloan Professor @Catherine Tucker which states that “network effects only really work as a source of competitive advantage if your product is also “sticky.” Scale will not bring future competitive advantage through network effects if your customers can all leave tomorrow.” If your product or platform is not sticky: https://hbr.org/2018/06/why-network-effects-matter-less-than-they-used-to If you are NOT SOLVING any real world problem or creating any new marketplace or changing the market behavior then one needs to really look into the use cases of these crypto businesses. I have heard pitches of crypto Uber, crypto air bnb…etc..I’m not sure if adding crypto to the already billion dollar existing businesses are solving any compelling problems. Not solving any real problem: Adding an additional feature with token economics to the existing legacy product or platform will not translate into huge customer base. One can make some noise that you are bringing down market giants and can attract the FOMO in bull market initially helping in raising millions in few seconds for your ICO, but how many customers would have really made the transition from those legacy system. People are creature of habits and “consumers’ existing habits are a key driver of resistance to new product use” — People are creature of habits: Habit slips: when consumers unintentionally resist new products So the question is how can we increase token adoption? Creating an environment where token adoption and usage is easy and consumer friendly is not the starting point. Building a community for the product that truly highlights the solution of the real world compelling problem is as important as working hard on those customer service deliveries and then going through every use case for problem solution and successful adoption. Some of the answers to this question of token adoption lies in the traditional startup building method that every successful founder has utilized and differentiated in their competitive analysis to build a loyal and scalable consumer base. Gartner Hype Cycle Blockchain Technology through Gartner Hype Cycle The maturity and adoption phase of the Gartner Hype Cycle provides insight into how new technology can solving real business problems and evolve through various use cases. Among the various phases of the Hype Cycle, we are currently entering the phase in Blockchain — when excitement wanes as some of the projects have failed to deliver, the adoption here is less than 5% (graph). “I ” We are looking at the third generation blockchain products how they can help to overcome the adoption chasm. “trough of disillusionment” nvestments continue only if the surviving projects improve their products to the satisfaction of early adopters. The steep entry barrier for common people trying to access crypto in exchanges and the esoteric nature of the blockchain technology reminds us of the early days of computer in 1980s when one needed to know DOS commands and various command line interfaces to interact with the computer. Things have changed with arrival of graphical user interfaces. Manuals for computer guides (if you want to read one ) issued are funny now and things of past. One does not need to provide such manuals to their granny or 2-3 year old child on how to use a computer or iphone. Blockchain needs to go through similar technological user advancement process to onboard more non-crypto savvy consumers. Till then, we need to focus how to decode these complexities and inconveniences whether it is remembering long private keys or the lack of accountability on stolen keys. here Blockchain is NOT THE SOLUTION for everything In a hyped-up bull market, the technology triggers new entrants to promote blockchain as a solution to everything. Irresponsible entrepreneurship and indisciplined investment are bad behaviors. Such behaviors deliberately manipulates the market and causes distortion in the market to the detriment of everyone. “Market analysts get bad or distorted data that don’t impart understanding of the real underlying market dynamics. Blockchain companies that would have made the best use of funds can go unfunded, sophisticated investors lose money, naive/retail investors lose everything.” Bitcoin was hatched as an antidote to the 2007 financial crisis. The idea was to protect the people from an unfair system whether it is the government or the financial system “ because they work in favor of those who are “too big to fail”, at the expense of those who are “too small to care about.” ” — as said by Ray Dillinger (who worked with Satoshi during early development of Bitcoin- Read the full interview here ) From that decentralized movement to many more centralized promotional campaigns and to commercial enterprise interests and some blockchain implementations, tokenization has come a long way. The new fundraising protocol has truly decentralized the way we raise money, but we can not slap blockchain to every project to get funded unless there is a well-researched real world use case solution that can not be solved through enriched database or simple digital ledger implementation. It will take time and collective innovation to build token adoption, till then let’s focus on compelling and viable use cases research based blockchain solution. NOTE: This article should not be considered financial or legal advice and is the opinion of the author. Gayatri Sarkar is a General Partner in Blockchain based venture capital fund SportVEST, focussed on investing in future of sports. SPORTVEST VC FUND: For more information on the SportVEST Private-Sale, contact us at: hello@sportvest.io or visit our website at: sportvest.io . Don’t forget to join us on Telegram and follow us on Twitter .

Blockchain Token Adoption Problem: why the network effect is not enough

My blogs chronicle my experience as a technologist making my way into Silicon Valley. I am a queer person of color, gender non-conforming, immigrant, and ex-foster youth. I come from a non-traditional coding background. I studied a few CS courses in college and ended up majoring in the humanities before becoming a teacher. Teaching web development to under served teens turned me on to coding. After finishing at a coding school, I began working at start ups in San Francisco. Half of my blogs are about technical subjects and the other half are about equality and tech access (my journey). Chose another tutorial from Brian Holt this week. What I love about learning from Brian is that he understands self-learning, well because he admits to dropping out of college and being self-taught. He explains everything in plain English, humbly acknowledges that the tools he uses or how he goes about solving a problem is an “opinion” and emphasizes that there are other ways of achieving the same result. He says things like, “The first time you learn recursion is tough. But once you get it, you start to realize that it is hard but it’s simple.” He also adds a caveat that this course is not a replacement for taking 4 of . I know dropping out of college and being ‘self-taught’ is kind of a running joke in the tech industry, if the show Silicon Valley is any indication of that: semesters comp sci Big Takeaways: Big O Recursion Sorting Algorithms Data Structures Big O Notation In simple terms, Big O is the measurement of the time it takes to perform x number of operations with n inputs. For example, if you have the equation, 3x² + x + 1. Big O takes into account the operation x² which gives us the largest number in the equation. The Big O for this equation then would be O(n²) because if we add up the operations O(n²) + O(1) + O(1) = O(2n²) and we drop the coefficient because it is inconsequential as the numbers get larger. Let’s try calculating the Big O using another function. var crossAdd = function(input) {var answer = [];for (var i = 0; i < input.length; i++){var goingUp = input[i];var goingDown = input[input.length-1-i];answer.push(goingUp + goingDown);}return answer;} Let’s calculate Big O, crossAdd is an assignment so that is O(1), so is answer. So we have, O(1) + O(1). Our for loop O(1) + O(n) + O(n). Another assignment, O(1) + O(n) + O(n) + O(1). In total, we have O(5) + O(4n). Since O(1) is a constant, we can drop the O(5). The coefficient in front of the n will matter less and less as the number gets larger so we are left with O(n). Try another: function makeTuples(input) {var answer = [];for (var i = 0; i < input.length; i++) {for (var j = 0; j < input.length; j++) {answer.push([input[i], input[j]]);}}return answer;} Keeping track of the operations that affect the program the most, the 2 for loops means we have to go through the inputs O(n) * O(n) times which equals O(n²). Recursion a function is calls itself inside of its code block must have a base case (breaks the function out of the recursive call) if your base case fails you get stack overflow — function is called until there is no more memory left in the stack (high cost, use for or while loops — iteration instead of recursion) When do we use recursion? Invoking a function multiple times entails overhead since the function’s stack frame must be created. This involves memory operations and CPU register updates, at the lowest level. Iteration / Loops require none of this additional work. If we have a program that requires a user to call a recursive function multiple times, this is not efficient, iteration would work better. function countdown(current) {if(current <= 0) return;wr(current);countdown(current-1)} Famous recursive function — Fibonacci function fibonacci(num) {if(num <= 2) {return 1} else {return fibonacci(num — 1) + fibonacci(num — 2)}} Equally famous recursive function — Factorial function factorial(num) {if(num < 2) {return 1} else {return num * factorial(num — 1)}} Sorting Algorithms //create a function that will swap numbers //write a function that will take a list, index1, and index2function swap(list, index1, index2) {let swapped = list[index1]list[index1] = list[index2]list[index2] = swapped} Bubble Sort — highly inefficient, because with an external and an internal loop, plus other constant operations, the algorithm’s Big O is equal to O(n²). //loop across the list to keep track if you are atthe beginning of the end//loop across the list to swap letters//return the new list function bubbleSort(items){let len = items.lengthfor (let i=0; i < len; i++){for (j=0, stop=len-i; j < stop; j++){if (items[j] > items[j+1]){swap(items, j, j+1);}}}return items;} 2. — more complex, occasionally useful. See pseudocode for explanation Insertion Sort //ex: [5, 3, 1] => inserts 3 into the 5 position, now 2 are sorted to [3, 5, 1] => makes a copy of 1 compares it to 5, moves 5 into its spot => look function insertionSort(array) {for(var i = 0; i < array.length; i++) {var temp = array[i];var j = i — 1;while (j >= 0 && array[j] > temp) {array[j + 1] = array[j];j — ;}array[j + 1] = temp;}return array;} 3. — divide and conquer, splits the array up into a left sorted and a right and then we put it back together Merge Sort function stitch(left, right) {const results = []while(left.length && right.length) {if(left[0] <= right[0]) {results.push(left.shift())} else {results.push(right.shift())}}while(left.length){results.push(left.shift())}while(right.length) {results.push(right.shift())}} Merge Sort function mergeSort(nums) {if(nums.length < 2) {return nums}const length = nums.lengthconst middle = Math.floor(length / 2)const left = nums.slice(0, middle)const right = nums.slice(middle, length)const sortedLEft = mergeSort(left)const sortedRight = mergeSort(right)return stitch(mergeSort(left), mergeSort(right))} 4. — another divide and conquer algorithm, most powerful sorting algorithm, recursive. The basic gist is that you take the last element in the list make that the pivot. Everything that’s less than the pivot gets put into a “left” list and everything that’s greater get’s put in a “right” list. You then make a recursive call for quick sort on the left and right lists, concatenate the sorted left list, the pivot, and then the right list (in that order.) The base case is when you have a length 1 or 0, return the list. Quick Sort function quicksortBasic(array) {if(array.length < 2) {return array;}var pivot = array[0];var lesser = [];var greater = [];for(var i = 1; i < array.length; i++) {if(array[i] < pivot) {lesser.push(array[i]);} else {greater.push(array[i]);}}return quicksortBasic(lesser).concat(pivot,quicksortBasic(greater));} Data Structures Array List vs. Linked Lists Array Lists — requires interacting with allocated pieces of memory. In Java, you have to declare the size of your array. Typical methods are: push, pop, find, and delete (_collapseTo which is a helper function for delete in this tutorial). Linked Lists — have a head, tail, next, and previous (if doubly linked). Linked lists are useful in avoiding data collisions via duplicate keys in a hash table. They are also useful in deletion and insertions. Binary Search Tree (BST) — Fast for searches, BSTs can have 0, 1, 2 children. Left is less than the parent node. Right is greater than the parent. Lookups are O(log n). BSTs are not used in production. Self-balancing trees are used in production. class Node {constructor(data, left=null, right=null) {this.data = datathis.left = leftthis.right = right}} class Tree {constructor() {this.root = null;} add(value) {if (this.root === null) {this.root = new Node(value);} else {let current = this.root;while(true) {if (current.value > value) {// go leftif (current.left) {current = current.left;} else {current.left = new Node(value);break;}} else {// go rightif (current.right) {current = current.right;} else {current.right = new Node(value);break;}}}}return this;} toJSON() {return JSON.stringify(this.root.serialize(), null, 4);} toObject() {return this.root.serialize();}} There was definitely a LOT of information in this tutorial and I was definitely drinking through the firehose trying to absorb it all. I WILL DEFINITELY be watching this again.

Four Semesters of Computer Science in Five Hours…Phew!

Overview: What is Etherbots? is a decentralized application hosted on the Ethereum network. It’s a Robot Wars inspired game where you collect parts and weapons, forge dynamic robots, and use them to battle others on the platform. Level up and discover rare parts by winning duels in the arena. Win — and you might receive a rare gold flamethrower to roast your enemies. Lose, and you’ll be rewarded with a common frying pan (which is about as useful as it would be dropping in the middle of Pochinki). Etherbots There are over two-million unique possible robots to create, with the scarcity of each immutably guaranteed by the smart contract. We loved CryptoKitties, but thought it was missing something. Crypto collectibles are fun — and groundbreaking — but we thought just breeding was more of a proof-of-concept than true game functionality. We wanted battling with interactive elements, social leaderboards, and an ability to build and customise your creations (and boast about them). Ethereum provides a unique and incredibly powerful platform to combine the guaranteed scarcity of collectibles with the functionality and competitiveness of a fully fleshed-out game. Combine that with a bit of creative inspiration from the old tv show Robot Wars (Sir Killalot OP), and you’ve got Etherbots! Customizability — Over Two Million Unique Robots Each robot is fully customisable and interchangeable. If you hate your turtle robot body — (you ) — sell it on the market it and snap up a sexy Mech T-Rex. monster You can combine any of the parts in your inventory to make a robot which can be used for battle (and gain bonuses if they’re of the same element or rarity — read below!) Battle Functionality & Rewards — Randomness on the Blockchain (AKA Boring Tech Stuff) A well-studied and often poorly understood topic is how to generate a good source of randomness when the hardware which runs the code are decentralised and deterministic — they all need to end up with the same result. We wanted to implement a solution for probabilistic battling which was random, game-theoretically safe, fit in with the narrative of the site, and was gas-efficient (*cough* oracles *cough*). Users submit a private string of text of their choice, which acts as the “instructions” for their robot. This is a string of 0’s and 1’s 32 characters in length — allowing for over 4 billion permutations, kept secret from the other player via a commit/reveal scheme. (Players can randomly generate this if they want, but we suggest ingesting your favourite substance and chanting maniacally until you find one which feels ). Both battling player’s secret strings are used to generate a seed which neither party can know without knowing the other player’s instructions — forming the basis for the random/probabilistic part of their battle. This probability will matter less the greater the disparity between their combat effectiveness, obviously! This seed can also be used to securely generate reward probabilities for the winner within the same transaction. just right Levelling & Perk Trees Our team is very much in love with the Fallout series (particularly New Vegas — sorry Bethesda). The perk tree system presented in this series, and others such as Skyrim, allows a high degree of personalisation and customisability, while rewarding the experience of levelling. While selling your crates or won parts is a core functionality of the game, we also wanted to reward users who regularly battle and use the platform in order to motivate true engagement. Thus there are two forms of levels in Etherbots: 1. Account Level Your account level allows you to accumulate experience independent of the robots you use to battle overtime. It persists with your Ethereum account and allows you to unlock rewards according to a perk tree. Maybe you want to invest in your fire perk tree, benefiting the combat capabilities of fire parts you use in battle; this could influence the way you decide to keep and sell parts after battles, too. 2. Part Level When you battle, your parts gain a little bit of experience (and more if you win). This directly affects the combat capability of those parts. Levels are intrinsic to parts, and stay with them even when they’re sold. Rarity & Types — Set Bonuses If you collect enough parts of one elemental type or rarity, and combine them into a robot, you’ll gain enhanced combat powers for that bot. Get a robot of the same type rarity (say, a legendary golden, fire element Etherbot) — and gain the highest set bonus possible. and Robot Crates We wanted to distribute a small amount of initial parts to users in a way that was incredibly fun, fair and rewarding, and had an element of luck due to part rarity. Crates provide players with a sense of for unlocking rare robots. pride and accomplishment Why the pre-sale? Why not just launch the full game? It’s hard to get a game like this started; you need to distribute robots in a fair way, and provide some sort of intrinsic reward for those who take the risk of getting in early. The entire game benefits from network effects and communal engagement, and rewarding early adopters — thereby turning them into advocates and fans who were with you from the beginning — is one of the best ways to create a thriving community around this game. Besides, loot crates are fun (as long as you don’t ruin the functionality by relying on huge amounts of microtransactions like Battlefront). Opening a satisfying loot crate literally feels like Christmas. Think about the animation of opening one up on Overwatch or League of Legends. Sure it’s harder to do this in the browser than in a unity engine rendered video game, but we want you to feel incredible when you open up and find what parts you received in your crate. Also — our loot crates provide you at the *very worst* with a full set of 4 parts, everything you need to create your own robot and battle. And these crates are hard-capped at 3900 to be sold, ever — even after the presale. However, all good things must come to an end. We’ve time-locked the presale contract to end 15 Feburary, which is when the main game will launch. Etherbots Crate Presale is Live Now! The source code is verified on Etherscan. We recommend interacting with the contract using MetaMask and . https://etherbots.io A crate opened by a very lucky user. Two gold parts has a spawn chance of 0.25 (5% each). Shadow parts have a 10% chance. Developing Etherbots was a labour-intensive task. It’s a work of love of many people, and we are constantly looking to integrate as much community feedback as possible. Don’t hesitate to contact us at support@etherbots.io, and we almost always have someone manning the live chat on . etherbots.io Join us on for live updates. Discord

Etherbots.io Launch — Decentralized Robot Wars on the blockchain

Personal Recollections of Joan of Arc — Volume 1 by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . My Exquisite Poem Goes to Smash here My Exquisite Poem Goes to Smash WE OF the personal staff were in fairyland now, during the few days that we waited for the return of the army. We went into society. To our two knights this was not a novelty, but to us young villagers it was a new and wonderful life. Any position of any sort near the person of the Maid of Vaucouleurs conferred high distinction upon the holder and caused his society to be courted; and so the D’Arc brothers, and Noel, and the Paladin, humble peasants at home, were gentlemen here, personages of weight and influence. It was fine to see how soon their country diffidences and awkwardnesses melted away under this pleasant sun of deference and disappeared, and how lightly and easily they took to their new atmosphere. The Paladin was as happy as it was possible for any one in this earth to be. His tongue went all the time, and daily he got new delight out of hearing himself talk. He began to enlarge his ancestry and spread it out all around, and ennoble it right and left, and it was not long until it consisted almost entirely of dukes. He worked up his old battles and tricked them out with fresh splendors; also with new terrors, for he added artillery now. We had seen cannon for the first time at Blois—a few pieces—here there was plenty of it, and now and then we had the impressive spectacle of a huge English bastille hidden from sight in a mountain of smoke from its own guns, with lances of red flame darting through it; and this grand picture, along with the quaking thunders pounding away in the heart of it, inflamed the Paladin’s imagination and enabled him to dress out those ambuscade-skirmishes of ours with a sublimity which made it impossible for any to recognize them at all except people who had not been there. You may suspect that there was a special inspiration for these great efforts of the Paladin’s, and there was. It was the daughter of the house, Catherine Boucher, who was eighteen, and gentle and lovely in her ways, and very beautiful. I think she might have been as beautiful as Joan herself, if she had had Joan’s eyes. But that could never be. There was never but that one pair, there will never be another. Joan’s eyes were deep and rich and wonderful beyond anything merely earthly. They spoke all the languages—they had no need of words. They produced all effects—and just by a glance, just a single glance; a glance that could convict a liar of his lie and make him confess it; that could bring down a proud man’s pride and make him humble; that could put courage into a coward and strike dead the courage of the bravest; that could appease resentments and real hatreds; that could make the doubter believe and the hopeless hope again; that could purify the impure mind; that could persuade—ah, there it is—persuasion! that is the word; what or who is it that it couldn’t persuade? The maniac of Domremy—the fairy-banishing priest—the reverend tribunal of Toul—the doubting and superstitious Laxart—the obstinate veteran of Vaucouleurs—the characterless heir of France—the sages and scholars of the Parliament and University of Poitiers—the darling of Satan, La Hire—the masterless Bastard of Orleans, accustomed to acknowledge no way as right and rational but his own—these were the trophies of that great gift that made her the wonder and mystery that she was. We mingled companionably with the great folk who flocked to the big house to make Joan’s acquaintance, and they made much of us and we lived in the clouds, so to speak. But what we preferred even to this happiness was the quieter occasions, when the formal guests were gone and the family and a few dozen of its familiar friends were gathered together for a social good time. It was then that we did our best, we five youngsters, with such fascinations as we had, and the chief object of them was Catherine. None of us had ever been in love before, and now we had the misfortune to all fall in love with the same person at the same time—which was the first moment we saw her. She was a merry heart, and full of life, and I still remember tenderly those few evenings that I was permitted to have my share of her dear society and of comradeship with that little company of charming people. The Paladin made us all jealous the first night, for when he got fairly started on those battles of his he had everything to himself, and there was no use in anybody else’s trying to get any attention. Those people had been living in the midst of real war for seven months; and to hear this windy giant lay out his imaginary campaigns and fairly swim in blood and spatter it all around, entertained them to the verge of the grave. Catherine was like to die, for pure enjoyment. She didn’t laugh loud—we, of course, wished she would—but kept in the shelter of a fan, and shook until there was danger that she would unhitch her ribs from her spine. Then when the Paladin had got done with a battle and we began to feel thankful and hope for a change, she would speak up in a way that was so sweet and persuasive that it rankled in me, and ask him about some detail or other in the early part of his battle which she said had greatly interested her, and would he be so good as to describe that part again and with a little more particularity?—which of course precipitated the whole battle on us, again, with a hundred lies added that had been overlooked before. I do not know how to make you realize the pain I suffered. I had never been jealous before, and it seemed intolerable that this creature should have this good fortune which he was so ill entitled to, and I have to sit and see myself neglected when I was so longing for the least little attention out of the thousand that this beloved girl was lavishing on him. I was near her, and tried two or three times to get started on some of the things that I had done in those battles—and I felt ashamed of myself, too, for stooping to such a business—but she cared for nothing but his battles, and could not be got to listen; and presently when one of my attempts caused her to lose some precious rag or other of his mendacities and she asked him to repeat, thus bringing on a new engagement, of course, and increasing the havoc and carnage tenfold, I felt so humiliated by this pitiful miscarriage of mine that I gave up and tried no more. The others were as outraged by the Paladin’s selfish conduct as I was—and by his grand luck, too, of course—perhaps, indeed, that was the main hurt. We talked our trouble over together, which was natural, for rivals become brothers when a common affliction assails them and a common enemy bears off the victory. Each of us could do things that would please and get notice if it were not for this person, who occupied all the time and gave others no chance. I had made a poem, taking a whole night to it—a poem in which I most happily and delicately celebrated that sweet girl’s charms, without mentioning her name, but any one could see who was meant; for the bare title—“The Rose of Orleans”—would reveal that, as it seemed to me. It pictured this pure and dainty white rose as growing up out of the rude soil of war and looking abroad out of its tender eyes upon the horrid machinery of death, and then—note this conceit—it blushes for the sinful nature of man, and turns red in a single night. Becomes a red rose, you see—a rose that was white before. The idea was my own, and quite new. Then it sent its sweet perfume out over the embattled city, and when the beleaguering forces smelt it they laid down their arms and wept. This was also my own idea, and new. That closed that part of the poem; then I put her into the similitude of the firmament—not the whole of it, but only part. That is to say, she was the moon, and all the constellations were following her about, their hearts in flames for love of her, but she would not halt, she would not listen, for ’twas thought she loved another. ’Twas thought she loved a poor unworthy suppliant who was upon the earth, facing danger, death, and possible mutilation in the bloody field, waging relentless war against a heartless foe to save her from an all too early grave, and her city from destruction. And when the sad pursuing constellations came to know and realize the bitter sorrow that was come upon them—note this idea—their hearts broke and their tears gushed forth, filling the vault of heaven with a fiery splendor, for those tears were falling stars. It was a rash idea, but beautiful; beautiful and pathetic; wonderfully pathetic, the way I had it, with the rhyme and all to help. At the end of each verse there was a two-line refrain pitying the poor earthly lover separated so far, and perhaps forever, from her he loved so well, and growing always paler and weaker and thinner in his agony as he neared the cruel grave—the most touching thing—even the boys themselves could hardly keep back their tears, the way Noel said those lines. There were eight four-line stanzas in the first end of the poem—the end about the rose, the horticultural end, as you may say, if that is not too large a name for such a little poem—and eight in the astronomical end—sixteen stanzas altogether, and I could have made it a hundred and fifty if I had wanted to, I was so inspired and so all swelled up with beautiful thoughts and fancies; but that would have been too many to sing or recite before a company that way, whereas sixteen was just right, and could be done over again if desired. The boys were amazed that I could make such a poem as that out of my own head, and so was I, of course, it being as much a surprise to me as it could be to anybody, for I did not know that it was in me. If any had asked me a single day before if it was in me, I should have told them frankly no, it was not. That is the way with us; we may go on half of our life not knowing such a thing is in us, when in reality it was there all the time, and all we needed was something to turn up that would call for it. Indeed, it was always so without family. My grandfather had a cancer, and they never knew what was the matter with him till he died, and he didn’t know himself. It is wonderful how gifts and diseases can be concealed in that way. All that was necessary in my case was for this lovely and inspiring girl to cross my path, and out came the poem, and no more trouble to me to word it and rhyme it and perfect it than it is to stone a dog. No, I should have said it was not in me; but it was. The boys couldn’t say enough about it, they were so charmed and astonished. The thing that pleased them the most was the way it would do the Paladin’s business for him. They forgot everything in their anxiety to get him shelved and silenced. Noel Rainguesson was clear beside himself with admiration of the poem, and wished he could do such a thing, but it was out of his line, and he couldn’t, of course. He had it by heart in half an hour, and there was never anything so pathetic and beautiful as the way he recited it. For that was just his gift—that and mimicry. He could recite anything better than anybody in the world, and he could take of La Hire to the very life—or anybody else, for that matter. Now I never could recite worth a farthing; and when I tried with this poem the boys wouldn’t let me finish; they would have nobody but Noel. So then, as I wanted the poem to make the best possible impression on Catherine and the company, I told Noel he might do the reciting. Never was anybody so delighted. He could hardly believe that I was in earnest, but I was. I said that to have them know that I was the author of it would be enough for me. The boys were full of exultation, and Noel said if he could just get one chance at those people it would be all he would ask; he would make them realize that there was something higher and finer than war-lies to be had here. But how to get the opportunity—that was the difficulty. We invented several schemes that promised fairly, and at last we hit upon one that was sure. That was, to let the Paladin get a good start in a manufactured battle, and then send in a false call for him, and as soon as he was out of the room, have Noel take his place and finish the battle himself in the Paladin’s own style, imitated to a shade. That would get great applause, and win the house’s favor and put it in the right mood to hear the poem. The two triumphs together with finish the Standard-Bearer—modify him, anyway, to a certainty, and give the rest of us a chance for the future. So the next night I kept out of the way until the Paladin had got his start and was sweeping down upon the enemy like a whirlwind at the head of his corps, then I stepped within the door in my official uniform and announced that a messenger from General La Hire’s quarters desired speech with the Standard-Bearer. He left the room, and Noel took his place and said that the interruption was to be deplored, but that fortunately he was personally acquainted with the details of the battle himself, and if permitted would be glad to state them to the company. Then without waiting for the permission he turned himself to the Paladin—a dwarfed Paladin, of course—with manner, tones, gestures, attitudes, everything exact, and went right on with the battle, and it would be impossible to imagine a more perfectly and minutely ridiculous imitation than he furnished to those shrieking people. They went into spasms, convulsions, frenzies of laughter, and the tears flowed down their cheeks in rivulets. The more they laughed, the more inspired Noel grew with his theme and the greater marvels he worked, till really the laughter was not properly laughing any more, but screaming. Blessedest feature of all, Catherine Boucher was dying with ecstasies, and presently there was little left of her but gasps and suffocations. Victory? It was a perfect Agincourt. The Paladin was gone only a couple of minutes; he found out at once that a trick had been played on him, so he came back. When he approached the door he heard Noel ranting in there and recognized the state of the case; so he remained near the door but out of sight, and heard the performance through to the end. The applause Noel got when he finished was wonderful; and they kept it up and kept it up, clapping their hands like mad, and shouting to him to do it over again. But Noel was clever. He knew the very best background for a poem of deep and refined sentiment and pathetic melancholy was one where great and satisfying merriment had prepared the spirit for the powerful contrast. So he paused until all was quiet, then his face grew grave and assumed an impressive aspect, and at once all faces sobered in sympathy and took on a look of wondering and expectant interest. Now he began in a low but distinct voice the opening verses of The Rose. As he breathed the rhythmic measures forth, and one gracious line after another fell upon those enchanted ears in that deep hush, one could catch, on every hand, half-audible ejaculations of “How lovely—how beautiful—how exquisite!” By this time the Paladin, who had gone away for a moment with the opening of the poem, was back again, and had stepped within the door. He stood there now, resting his great frame against the wall and gazing toward the reciter like one entranced. When Noel got to the second part, and that heart-breaking refrain began to melt and move all listeners, the Paladin began to wipe away tears with the back of first one hand and then the other. The next time the refrain was repeated he got to snuffling, and sort of half sobbing, and went to wiping his eyes with the sleeves of his doublet. He was so conspicuous that he embarrassed Noel a little, and also had an ill effect upon the audience. With the next repetition he broke quite down and began to cry like a calf, which ruined all the effect and started many to the audience to laughing. Then he went on from bad to worse, until I never saw such a spectacle; for he fetched out a towel from under his doublet and began to swab his eyes with it and let go the most infernal bellowings mixed up with sobbings and groanings and retchings and barkings and coughings and snortings and screamings and howlings—and he twisted himself about on his heels and squirmed this way and that, still pouring out that brutal clamor and flourishing his towel in the air and swabbing again and wringing it out. Hear? You couldn’t hear yourself think. Noel was wholly drowned out and silenced, and those people were laughing the very lungs out of themselves. It was the most degrading sight that ever was. Now I heard the clankety-clank that plate-armor makes when the man that is in it is running, and then alongside my head there burst out the most inhuman explosion of laughter that ever rent the drum of a person’s ear, and I looked, and it was La Hire; and the stood there with his gauntlets on his hips and his head tilted back and his jaws spread to that degree to let out his hurricanes and his thunders that it amounted to indecent exposure, for you could see everything that was in him. Only one thing more and worse could happen, and it happened: at the other door I saw the flurry and bustle and bowings and scrapings of officials and flunkeys which means that some great personage is coming—then Joan of Arc stepped in, and the house rose! Yes, and tried to shut its indecorous mouth and make itself grave and proper; but when it saw the Maid herself go to laughing, it thanked God for this mercy and the earthquake that followed. Such things make a life of bitterness, and I do not wish to dwell upon them. The effect of the poem was spoiled. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mark Twain (2004). Personal Recollections of Joan of Arc — Volume 1. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/2874/pg2874-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

My Exquisite Poem Goes to Smash

The library to be introduced - - is a Kafka Streams State Store implementation that persists data to . Java thriving-dev/kafka-streams-cassandra-state-store Apache Cassandra It's a 'drop-in' replacement for the official Kafka Streams state store solutions, notably (default) and . RocksDB InMemory By moving the state to an datastore the (from a deployment point of view) - which greatly . external stateful streams app effectively becomes stateless improves elasticity, and reduces rebalancing downtimes & failure recovery Cassandra/ScyllaDB is horizontally scalable and allows for which provides a boost to your existing Kafka Streams application with very little change to the source code. huge amounts of data In addition to the this post will also cover all out-of-the-box state store solutions, and explain individual characteristics, benefits, drawbacks, and limitations in detail. CassandraKeyValueStore ⏯️ Following the introduction and getting started guide, there's also a available. If you don't want to wait, feel free to head over to the . demo Thriving.dev YouTube Channel The first public release was on 9 January 2023. When writing this blog post the latest version was: - available on ! 0.4.0 Maven Central Table of contents Basics Recap Purpose Usage Example Under the Hood Known Limitations Next Steps Conclusion Basics Recap (Feel free to skip straight to the next section if you're already familiar with Kafka Streams and Apache Cassandra…) Kafka Streams Quoting : Apache Kafka - Wikipedia “Kafka Streams (or Streams API) is a stream-processing library written in Java. It was added in the Kafka 0.10.0.0 release. The library allows for the development of stateful stream-processing applications that are scalable, elastic, and fully fault-tolerant. The main API is a stream-processing  domain-specific language  (DSL) that offers high-level operators like filter,  map, grouping, windowing, aggregation, joins, and the notion of tables. Additionally, the Processor API can be used to implement custom operators for a more low-level development approach. The DSL and Processor API can be mixed, too. For stateful stream processing, Kafka Streams uses  RocksDB  to maintain local operator state. Because RocksDB can write to disk, the maintained state can be larger than available main memory. For fault-tolerance, all updates to local state stores are also written into a topic in the Kafka cluster. This allows recreating state by reading those topics and feed all data into RocksDB.” In case you are entirely new to Kafka Streams, I recommend getting started by reading some official materials provided by Confluent, e.g. Introduction Kafka Streams API Apache Cassandra Quoting : Apache Cassandra - Wikipedia “Apache Cassandra is a free and open-source, distributed, wide-column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients.” Purpose While Wikipedia’s summary (see above) only mentions RocksDB, Kafka Streams ships with following implementations: KeyValueStore org.apache.kafka.streams.state.internals.RocksDBStore org.apache.kafka.streams.state.internals.InMemoryKeyValueStore org.apache.kafka.streams.state.internals.MemoryLRUCache Let’s look at the traits of each store implementation in more detail… RocksDBStore RocksDB is the default state store for Kafka Streams. The is a key-value store based on (surprise!). State is flushed to disk, allowing the state to exceed the size of available memory. RocksDBStore persistent RocksDB Since the state is persisted to disk, it can be re-used and does not need to be restored (changelog topic replay) when the application instance comes up after a restart (e.g. following an upgrade, instance migration, or failure). The RocksDB state store provides good performance and is well configured out of the box, but might need to be tuned for certain use cases (which is no small feat and requires an understanding of RocksDB configuration). Writing to and reading from disk comes with I/O, for performance reasons buffering and caching patterns are in place. The record cache (on heap) is particularly useful for optimizing writes by reducing the number of updates to local state and changelog topics. The RocksDB block cache (off heap) optimizes reads. In a typical modern setup, stateful Kafka Streams applications run on Kubernetes as a with persistent state stores (RocksDB) on . StatefulSet PersistentVolumes InMemoryKeyValueStore The , as the name suggests, maintains state (RAM). InMemoryKeyValueStore in-memory One obvious benefit is that the pure in-memory stores come with good performance (operates in RAM…). Further, hosting and operating are simpler compared to RocksDB, since there is no requirement to provide and manage disks. Drawbacks to having the store in-memory are limitations in store size and increased infrastructure costs (RAM is more expensive than disk storage). Further, state always is lost on application restart and therefore first needs to be restored from changelog topics (recovery takes longer). When low rebalance downtimes / quick recovery is concerned, using standby replicas ( ) help to reduce recovery time. num.standby.replicas MemoryLRUCache ( ) Stores.lruMap The is an store based on . The term comes from the (least recently used) behavior combined with the cap (per streams task!). MemoryLRUCache in-memory HashMap cache LRU maxCacheSize It’s a rather uncommon choice but can be a valid fit for certain use cases. Same as the state always is lost on application restart and is restored from changelog topics. InMemoryKeyValueStore applies client-side only (in-memory HashMap, per streams task state store -> the least recently used entry is dropped when the underlying HashMap’s capacity is breached) but does not ‘cleanup’ the changelog topic (send ). The ( ) changelog topic keeps growing in size while the state available to processing is constrained by . maxCacheSize tombstones compacted maxCacheSize Therefore, it is recommended to use in combination with custom changelog topic config (also ) to have a time-based retention in place that satisfies your functional data requirements (if possible). cleanup.policy=[compact,delete] retention.ms The is applied per streams task (~input topic partitions), so take into consideration when calculating total capacity, memory requirements per app instance, … maxCacheSize CassandraKeyValueStore Now finally we get to the subject of this blog post, the custom implementation of a state store that persists data to Apache Cassandra. With data is in an external database -> Apache Cassandra <- *or compatible solutions (e.g. ScyllaDB). Apache Cassandra is a distributed, clustered data store that allows to scale horizontally to enable up to Petabytes of data, thus can be accommodated. CassandraKeyValueStore persistently stored very large Kafka Streams state Moving the state into an data store - outside the application so to say - allows you to effectively run the app in a fashion. Further, with logging disabled, there's -> required which enables fluent rebalancing and helps and . external stateless no changelog topic no state restore reduce rebalance downtimes recovery time This greatly of your application, which opens up more possibilities such as e.g. efficient & fluent autoscaling... improves the elasticity and scalability It can also help ease/avoid known problems with the 'Kafka Streams’ specific task assignment such as 'uneven load distribution' and 'idle consumers' (I'm thinking about writing a separate blog post on these issues...). Kafka Streams property allows to achieve low rebalance downtimes by telling the consumers to send a request to the group leader on graceful shutdown. internal.leave.group.on.close=true LeaveGroup For more information on such kafka internals I can recommend to watch+read following Confluent developer guide: . Consumer Group Protocol Note that this property is also used & explained in the . demo ⚠ Please be aware this is an in-official property (not part of the public API), thus can be deprecated or dropped any time. Adding an external, 3rd party Software to the heart (or rather stomach?) to your stream processing application, adds a new, additional to your architecture. single point of failure Usage Example Get it! The artifact is available on Maven Central: Maven <dependency>
    <groupId>dev.thriving.oss</groupId>
    <artifactId>kafka-streams-cassandra-state-store</artifactId>
    <version>${version}</version>
</dependency> Gradle (Groovy DSL) implementation 'dev.thriving.oss:kafka-streams-cassandra-state-store:${version}’ Classes of this library are in the package . dev.thriving.oss.kafka.streams.cassandra.state.store Quick Start High-level DSL <> StoreSupplier When using the high-level DSL, i.e., , users create s that can be further customized via . StreamsBuilder StoreSupplier Materialized For example, a topic read as can be materialized into a Cassandra k/v store with custom key/value Serdes, with logging and caching disabled: KTable StreamsBuilder builder = new StreamsBuilder();
KTable<Long,String> table = builder.table(
  "topicName",
  Materialized.<Long,String>as(
                 CassandraStores.builder(session, "store-name")
                         .keyValueStore()
              )
              .withKeySerde(Serdes.Long())
              .withValueSerde(Serdes.String())
              .withLoggingDisabled()
              .withCachingDisabled()); Processor API <> StoreBuilder When using the Processor API, i.e., , users create s that can be attached to s. Topology StoreBuilder Processor For example, you can create a Cassandra with custom Serdes, logging, and caching disabled like: KeyValueStore<String, Long> Topology topology = new Topology();

StoreBuilder<KeyValueStore<String, Long>> storeBuilder = Stores.keyValueStoreBuilder(
                CassandraStores.builder(session, "store-name")
                        .keyValueStore(),
                Serdes.String(),
                Serdes.Long())
        .withLoggingDisabled()
        .withCachingDisabled();

topology.addStateStore(storeBuilder); Demo Features the notorious ( ), written as a , running in a fully . 'word-count example' ref quarkus application clustered docker-compose localstack Source code for this demo: kafka-streams-cassandra-state-store/examples/word-count-quarkus (at 0.4.0) https://www.youtube.com/watch?v=2Co9-8E-uJE&embedable=true Store Types kafka-streams-cassandra-state-store comes with 2 different store types: keyValueStore globalKeyValueStore keyValueStore (recommended default) A persistent . The underlying cassandra table is the store context . Therefore, all CRUD operations against this store always query by and return results for a single stream task. KeyValueStore<Bytes, byte[]> partitioned by task partition globalKeyValueStore A persistent . The underlying cassandra table uses the . Therefore, all CRUD operations against this store work from any streams task and therefore always are “global”. KeyValueStore<Bytes, byte[]> record key as sole /PRIMARY KEY/ Due to the nature of cassandra tables having a single PK (no clustering key), this store supports only a limited number of operations. If you're planning to use this store type, please make sure to get a full understanding of the specifics by reading the to understand its behaviour. relevant docs Advanced For more detailed documentation, please visit the GitHub project… Store types, supported operations Builder usage + config options Under the hood Implemented/compiled with Java 17 kafka-streams 3.4 datastax java-driver-core 4.15.0 Supported client-libs Kafka Streams 2.7.0+ (maybe even earlier versions, but wasn’t tested further back) Datastax java client (v4) 'com.datastax.oss:java-driver-core:4.15.0' ScyllaDB shard-aware datastax java client (v4) fork 'com.scylladb:java-driver-core:4.14.1.0' Supported databases Apache Cassandra 3.11 Apache Cassandra 4.0, 4.1 ScyllaDB (should work from 4.3+) Underlying CQL Schema keyValueStore Using defaults, for a state store named "word-count" the following CQL Schema applies: CREATE TABLE IF NOT EXISTS word_count_kstreams_store (
    partition int,
    key blob,
    time timestamp,
    value blob,
    PRIMARY KEY ((partition), key)
) WITH compaction = { 'class' : 'LeveledCompactionStrategy' } globalKeyValueStore Using defaults, for a state store named "clicks-global" the following CQL Schema applies: CREATE TABLE IF NOT EXISTS clicks_global_kstreams_store (
    key blob,
    time timestamp,
    value blob,
    PRIMARY KEY (key)
) WITH compaction = { 'class' : 'LeveledCompactionStrategy' } Feat: Cassandra table with default TTL Cassandra has a table option (default expiration time (“TTL”) in seconds for a table) which can be useful for certain use cases where data (state) expires after a known period. default_time_to_live ℹ️ that writes to Cassandra are made with system time. The table based on -> the time of the current record being processed ( ). Please note TTL is applied 'time of write' != stream time The can be defined via the builder method, e.g.: default_time_to_live withTableOptions CassandraStores.builder(session, "word-grouped-count")
        .withTableOptions("""
                compaction = { 'class' : 'LeveledCompactionStrategy' }
                AND default_time_to_live = 86400
                """)
        .keyValueStore() Cassandra table partitioning (avoiding large partitions) Kafka is persisting data in segments and is built for sequential r/w. As long as there’s sufficient disk storage space available to brokers, a high number of messages for a single topic partition is not a problem. Apache Cassandra on the other hand can get inefficient (up to severe failures such as load shedding, dropped messages, and crashed and downed nodes) when the partition size grows too large. The reason is that searching becomes too slow as the search within a partition is slow. Also, it puts a lot of pressure on the (JVM) heap. The community has offered a standard recommendation for Cassandra users to keep Partitions under 400MB, and preferably under 100MB. For the current implementation, the Cassandra table created for the ‘default’ key-value store is partitioned by the Kafka (“wide partition pattern”). partition key Please keep these issues in mind when working with relevant data volumes. In case you don’t need to query your store / only lookup by key (‘range’, ‘prefixScan’; ref ) it’s recommended to use rather than since it is partitioned by the (:= primary key). Supported operations by store type globalKeyValueStore keyValueStore event key References blog post on Wide Partitions in Apache Cassandra 3.11 in case anyone has funded knowledge if/how this has changed with Cassandra 4, please share in the comments below!! Note: stackoverflow question Known Limitations Adding additional infrastructure for data persistence external to Kafka comes with certain risks and constraints. Consistency Kafka Streams supports and processing guarantees. At-least-once semantics is enabled by default. at-least-once exactly-once Kafka Streams processing guarantees is using Kafka transactions. These transactions wrap the entirety of processing a message throughout your streams topology, including messages published to outbound topic(s), changelog topic(s), and consumer offsets topic(s). exactly-once This is possible through transactional interaction with a single distributed system (Apache Kafka). Bringing an external system (Cassandra) into play breaks this pattern. Once data is written to the database it can’t be rolled back in the event of a subsequent error / failure to complete the current message processing. => If you need strong consistency, have processing enabled (streams config: ), and/or your processing logic is not fully idempotent then using is discouraged! exactly-once processing.guarantee="exactly_once_v2" kafka-streams-cassandra-state-store ℹ️ Please note this is also the case when using kafka-streams with the native state stores (RocksDB/InMemory) with processing.guarantee (default). at-least-once For more information on Kafka Streams processing guarantees, check the sources referenced below. https://medium.com/lydtech-consulting/kafka-streams-transactions-exactly-once-messaging-82194b50900a https://docs.confluent.io/platform/current/streams/developer-guide/config-streams.html#processing-guarantee https://docs.confluent.io/platform/current/streams/concepts.html#processing-guarantees Incomplete Implementation of Interfaces StateStore For now, only is supported (vs. e.g. / ).
Also, not all methods have been implemented. KeyValueStore WindowStore SessionStore Next Steps Here are some of the tasks (high level) in the current backlog: Features Implement (coming soon with Kafka 3.5.0 release) KIP-889: Versioned State Stores Add a simple (optional) InMemory read cache -> ? Caffeine Support / WindowStore SessionStore Non-functional Benchmark Add metrics Ops GitHub actions to release + publish to maven central (snapshot / releases) Add Renovate Conclusion It's been a fun journey so far, starting from an initial POC to a working library published to maven central - though still to be considered 'experimental', since it's not been production-tested yet. The out-of-the-box state stores satisfy most requirements, no need to switch without necessity.
Still, it's a usable piece of software that may fill a gap for specific requirements. I'm looking forward to working on the next steps such as benchmarking/ . load testing Feedback is very welcome, also, if you are planning to, or have decided to use the library in a project, please leave a comment below. Footnotes At the time of writing this blog post, the latest versions of relevant libs were: Kafka / Streams API: 3.4.0 Cassandra java-driver-core: 4.15.0 kafka-streams-cassandra-state-store: 0.4.0 References 4. Stateful Processing - Mastering Kafka Streams and ksqlDB Book Kafka Streams Memory Management | Confluent Documentation Also published . here

Unveiling Kafka-Streams-Cassandra-State-Store

THE PERFORMANCE

A Princess of Mars by Edgar Rice Burroughs, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . ON THE ARIZONA HILLS ON THE ARIZONA HILLS I am a very old man; how old I do not know. Possibly I am a hundred, possibly more; but I cannot tell because I have never aged as other men, nor do I remember any childhood. So far as I can recollect I have always been a man, a man of about thirty. I appear today as I did forty years and more ago, and yet I feel that I cannot go on living forever; that some day I shall die the real death from which there is no resurrection. I do not know why I should fear death, I who have died twice and am still alive; but yet I have the same horror of it as you who have never died, and it is because of this terror of death, I believe, that I am so convinced of my mortality. And because of this conviction I have determined to write down the story of the interesting periods of my life and of my death. I cannot explain the phenomena; I can only set down here in the words of an ordinary soldier of fortune a chronicle of the strange events that befell me during the ten years that my dead body lay undiscovered in an Arizona cave. I have never told this story, nor shall mortal man see this manuscript until after I have passed over for eternity. I know that the average human mind will not believe what it cannot grasp, and so I do not purpose being pilloried by the public, the pulpit, and the press, and held up as a colossal liar when I am but telling the simple truths which some day science will substantiate. Possibly the suggestions which I gained upon Mars, and the knowledge which I can set down in this chronicle, will aid in an earlier understanding of the mysteries of our sister planet; mysteries to you, but no longer mysteries to me. My name is John Carter; I am better known as Captain Jack Carter of Virginia. At the close of the Civil War I found myself possessed of several hundred thousand dollars (Confederate) and a captain’s commission in the cavalry arm of an army which no longer existed; the servant of a state which had vanished with the hopes of the South. Masterless, penniless, and with my only means of livelihood, fighting, gone, I determined to work my way to the southwest and attempt to retrieve my fallen fortunes in a search for gold. I spent nearly a year prospecting in company with another Confederate officer, Captain James K. Powell of Richmond. We were extremely fortunate, for late in the winter of 1865, after many hardships and privations, we located the most remarkable gold-bearing quartz vein that our wildest dreams had ever pictured. Powell, who was a mining engineer by education, stated that we had uncovered over a million dollars worth of ore in a trifle over three months. As our equipment was crude in the extreme we decided that one of us must return to civilization, purchase the necessary machinery and return with a sufficient force of men properly to work the mine. As Powell was familiar with the country, as well as with the mechanical requirements of mining we determined that it would be best for him to make the trip. It was agreed that I was to hold down our claim against the remote possibility of its being jumped by some wandering prospector. On March 3, 1866, Powell and I packed his provisions on two of our burros, and bidding me good-bye he mounted his horse, and started down the mountainside toward the valley, across which led the first stage of his journey. The morning of Powell’s departure was, like nearly all Arizona mornings, clear and beautiful; I could see him and his little pack animals picking their way down the mountainside toward the valley, and all during the morning I would catch occasional glimpses of them as they topped a hog back or came out upon a level plateau. My last sight of Powell was about three in the afternoon as he entered the shadows of the range on the opposite side of the valley. Some half hour later I happened to glance casually across the valley and was much surprised to note three little dots in about the same place I had last seen my friend and his two pack animals. I am not given to needless worrying, but the more I tried to convince myself that all was well with Powell, and that the dots I had seen on his trail were antelope or wild horses, the less I was able to assure myself. Since we had entered the territory we had not seen a hostile Indian, and we had, therefore, become careless in the extreme, and were wont to ridicule the stories we had heard of the great numbers of these vicious marauders that were supposed to haunt the trails, taking their toll in lives and torture of every white party which fell into their merciless clutches. Powell, I knew, was well armed and, further, an experienced Indian fighter; but I too had lived and fought for years among the Sioux in the North, and I knew that his chances were small against a party of cunning trailing Apaches. Finally I could endure the suspense no longer, and, arming myself with my two Colt revolvers and a carbine, I strapped two belts of cartridges about me and catching my saddle horse, started down the trail taken by Powell in the morning. As soon as I reached comparatively level ground I urged my mount into a canter and continued this, where the going permitted, until, close upon dusk, I discovered the point where other tracks joined those of Powell. They were the tracks of unshod ponies, three of them, and the ponies had been galloping. I followed rapidly until, darkness shutting down, I was forced to await the rising of the moon, and given an opportunity to speculate on the question of the wisdom of my chase. Possibly I had conjured up impossible dangers, like some nervous old housewife, and when I should catch up with Powell would get a good laugh for my pains. However, I am not prone to sensitiveness, and the following of a sense of duty, wherever it may lead, has always been a kind of fetich with me throughout my life; which may account for the honors bestowed upon me by three republics and the decorations and friendships of an old and powerful emperor and several lesser kings, in whose service my sword has been red many a time. About nine o’clock the moon was sufficiently bright for me to proceed on my way and I had no difficulty in following the trail at a fast walk, and in some places at a brisk trot until, about midnight, I reached the water hole where Powell had expected to camp. I came upon the spot unexpectedly, finding it entirely deserted, with no signs of having been recently occupied as a camp. I was interested to note that the tracks of the pursuing horsemen, for such I was now convinced they must be, continued after Powell with only a brief stop at the hole for water; and always at the same rate of speed as his. I was positive now that the trailers were Apaches and that they wished to capture Powell alive for the fiendish pleasure of the torture, so I urged my horse onward at a most dangerous pace, hoping against hope that I would catch up with the red rascals before they attacked him. Further speculation was suddenly cut short by the faint report of two shots far ahead of me. I knew that Powell would need me now if ever, and I instantly urged my horse to his topmost speed up the narrow and difficult mountain trail. I had forged ahead for perhaps a mile or more without hearing further sounds, when the trail suddenly debouched onto a small, open plateau near the summit of the pass. I had passed through a narrow, overhanging gorge just before entering suddenly upon this table land, and the sight which met my eyes filled me with consternation and dismay. The little stretch of level land was white with Indian tepees, and there were probably half a thousand red warriors clustered around some object near the center of the camp. Their attention was so wholly riveted to this point of interest that they did not notice me, and I easily could have turned back into the dark recesses of the gorge and made my escape with perfect safety. The fact, however, that this thought did not occur to me until the following day removes any possible right to a claim to heroism to which the narration of this episode might possibly otherwise entitle me. I do not believe that I am made of the stuff which constitutes heroes, because, in all of the hundreds of instances that my voluntary acts have placed me face to face with death, I cannot recall a single one where any alternative step to that I took occurred to me until many hours later. My mind is evidently so constituted that I am subconsciously forced into the path of duty without recourse to tiresome mental processes. However that may be, I have never regretted that cowardice is not optional with me. In this instance I was, of course, positive that Powell was the center of attraction, but whether I thought or acted first I do not know, but within an instant from the moment the scene broke upon my view I had whipped out my revolvers and was charging down upon the entire army of warriors, shooting rapidly, and whooping at the top of my lungs. Singlehanded, I could not have pursued better tactics, for the red men, convinced by sudden surprise that not less than a regiment of regulars was upon them, turned and fled in every direction for their bows, arrows, and rifles. The view which their hurried routing disclosed filled me with apprehension and with rage. Under the clear rays of the Arizona moon lay Powell, his body fairly bristling with the hostile arrows of the braves. That he was already dead I could not but be convinced, and yet I would have saved his body from mutilation at the hands of the Apaches as quickly as I would have saved the man himself from death. Riding close to him I reached down from the saddle, and grasping his cartridge belt drew him up across the withers of my mount. A backward glance convinced me that to return by the way I had come would be more hazardous than to continue across the plateau, so, putting spurs to my poor beast, I made a dash for the opening to the pass which I could distinguish on the far side of the table land. The Indians had by this time discovered that I was alone and I was pursued with imprecations, arrows, and rifle balls. The fact that it is difficult to aim anything but imprecations accurately by moonlight, that they were upset by the sudden and unexpected manner of my advent, and that I was a rather rapidly moving target saved me from the various deadly projectiles of the enemy and permitted me to reach the shadows of the surrounding peaks before an orderly pursuit could be organized. My horse was traveling practically unguided as I knew that I had probably less knowledge of the exact location of the trail to the pass than he, and thus it happened that he entered a defile which led to the summit of the range and not to the pass which I had hoped would carry me to the valley and to safety. It is probable, however, that to this fact I owe my life and the remarkable experiences and adventures which befell me during the following ten years. My first knowledge that I was on the wrong trail came when I heard the yells of the pursuing savages suddenly grow fainter and fainter far off to my left. I knew then that they had passed to the left of the jagged rock formation at the edge of the plateau, to the right of which my horse had borne me and the body of Powell. I drew rein on a little level promontory overlooking the trail below and to my left, and saw the party of pursuing savages disappearing around the point of a neighboring peak. I knew the Indians would soon discover that they were on the wrong trail and that the search for me would be renewed in the right direction as soon as they located my tracks. I had gone but a short distance further when what seemed to be an excellent trail opened up around the face of a high cliff. The trail was level and quite broad and led upward and in the general direction I wished to go. The cliff arose for several hundred feet on my right, and on my left was an equal and nearly perpendicular drop to the bottom of a rocky ravine. I had followed this trail for perhaps a hundred yards when a sharp turn to the right brought me to the mouth of a large cave. The opening was about four feet in height and three to four feet wide, and at this opening the trail ended. It was now morning, and, with the customary lack of dawn which is a startling characteristic of Arizona, it had become daylight almost without warning. Dismounting, I laid Powell upon the ground, but the most painstaking examination failed to reveal the faintest spark of life. I forced water from my canteen between his dead lips, bathed his face and rubbed his hands, working over him continuously for the better part of an hour in the face of the fact that I knew him to be dead. I was very fond of Powell; he was thoroughly a man in every respect; a polished southern gentleman; a staunch and true friend; and it was with a feeling of the deepest grief that I finally gave up my crude endeavors at resuscitation. Leaving Powell’s body where it lay on the ledge I crept into the cave to reconnoiter. I found a large chamber, possibly a hundred feet in diameter and thirty or forty feet in height; a smooth and well-worn floor, and many other evidences that the cave had, at some remote period, been inhabited. The back of the cave was so lost in dense shadow that I could not distinguish whether there were openings into other apartments or not. As I was continuing my examination I commenced to feel a pleasant drowsiness creeping over me which I attributed to the fatigue of my long and strenuous ride, and the reaction from the excitement of the fight and the pursuit. I felt comparatively safe in my present location as I knew that one man could defend the trail to the cave against an army. I soon became so drowsy that I could scarcely resist the strong desire to throw myself on the floor of the cave for a few moments’ rest, but I knew that this would never do, as it would mean certain death at the hands of my red friends, who might be upon me at any moment. With an effort I started toward the opening of the cave only to reel drunkenly against a side wall, and from there slip prone upon the floor. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Edgar Rice Burroughs (1993). A Princess of Mars. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/62/pg62-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html . lead image was generated using HackerNoon's Stable Disffusion Al Image Generator feature, via the prompt "Arizona Hills".

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