Matterless

matterless

Time Data Series: Continuing the Rest of the Story

Часавыя шэрагі даных: працяг астатняй часткі гісторыі

Времеви серии от данни: Продължаване на останалата част от историята

Series de datos temporales: continuando el resto de la historia

Série de données temporelles : suite de l'histoire

टाइम डेटा सीरीज़: कहानी का बाकी हिस्सा जारी

Időadat-sorozat: A történet további részének folytatása

時間データシリーズ: 物語の続き

დროის მონაცემების სერია: დანარჩენი ისტორიის გაგრძელება

ស៊េរីទិន្នន័យពេលវេលា៖ បន្តរឿងរ៉ាវដែលនៅសល់

Time Data Series: Kuenderera mberi Imwe Nyaya

Tidsdataserie: Fortsätter resten av berättelsen

Силсилаи маълумотҳои вақт: Идомаи боқимондаи ҳикоя

Unitarity Bound on Dark Matter in Low-temperature Reheating Scenarios: Acknowledgments and Reference

নিম্ন-তাপমাত্রা পুনঃউষ্ণকরণের পরিস্থিতিতে ডার্ক ম্যাটারে ঐক্যবদ্ধতা: স্বীকৃতি এবং রেফারেন্স

Unitaritätsgrenze für Dunkle Materie in Niedertemperatur-Wiedererwärmungsszenarien: Danksagungen und Referenz

Unitaridad unida a la materia oscura en escenarios de recalentamiento a baja temperatura: reconocimientos y referencias

Unitarité liée à la matière noire dans les scénarios de réchauffage à basse température : remerciements et référence

निम्न-तापमान पुनःतापन परिदृश्यों में डार्क मैटर पर यूनिटैरिटी बाउंड: आभार और संदर्भ

低温再加熱シナリオにおける暗黒物質のユニタリー性限界: 謝辞と参考文献

저온 재가열 시나리오에서 암흑물질에 결합된 단일성: 승인 및 참고자료

Unitariedade vinculada à matéria escura em cenários de reaquecimento de baixa temperatura: reconhecimentos e referências

Унитарность, связанная с темной материей в сценариях низкотемпературного разогрева: благодарности и ссылки

Düşük Sıcaklıkta Yeniden Isıtma Senaryolarında Karanlık Maddeye Bağlı Üniterlik: Teşekkür ve Referans

Tính thống nhất bị ràng buộc đối với Vật chất tối trong các kịch bản hâm nóng ở nhiệt độ thấp: Lời cảm ơn và tài liệu tham khảo

低温再加热场景中暗物质的幺正性界限：致谢和参考文献

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?”

Stories, News & Mentions about MATTERLESS

Matterless’s Stories on HackerNoon