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You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 20 of 38. Line of Effort 3: Ensure International Standards Processes are Transparent, Open, Inclusive, and Impartial International technology standards facilitate technology advancement, trade, global economic growth, and market access, particularly for startups and small- and medium-sized enterprises. They are also an area of strategic and economic competition, with the PRC in particular pushing top-down approaches to standards development process and using its economic influence to compel support for its standard proposals. In May 2023, the Biden-Harris White House published the first ever U.S. Government National Standards Strategy for Critical and Emerging Technology (USG NSSCET). As outlined in the USG NSSCET, the United States will work with allies, partners, the private sector, and civil society to ensure that international standards development embraces transparency, openness, impartiality and consensus, effectiveness and relevance, coherence, and broad multistakeholder participation. The Department of State, in cooperation with the Department of Commerce and other agencies, is building enhanced capacity to engage directly in international standards development organizations and to coordinate with industry and civil society to ensure robust participation by U.S. stakeholders in standards making processes. Working with the FCC, NIST, National Telecommunications and Information Administration (NTIA), and other federal agencies, the Department of State supports standards development processes for a wide range of critical and emerging technologies and platforms, including IoT, energy grids, smart cities, and connected vehicles. The United States will continue to promote and leverage cybersecurity and privacy standards and guidelines developed by NIST through open processes with a strong connection to international standards. This approach reinforces the U.S. policy for standards: a private-sector led, industry-driven approach with government participation that emphasizes the use of international standards developed in open, transparent, and consensus-based processes. This alignment helps stakeholders reduce the burden of international regulatory and legal regimes, leading to a reduced cost of operation and a greater understanding of international policies. It also highlights the value of a bottom-up approach for other governments as they develop their cybersecurity priorities. The U.S. government has developed formal and informal methods of information sharing and standards development monitoring through regular engagement with partners and allies. Quad partners and members of the TTC, for example, have signed memoranda of cooperation to enable increased information sharing, coordination, and influence in international standards development. The Department of State has also supported increasing participation in standards development organizations from historically underrepresented nations. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 20 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 20 of 38. here Line of Effort 3: Ensure International Standards Processes are Transparent, Open, Inclusive, and Impartial International technology standards facilitate technology advancement, trade, global economic growth, and market access, particularly for startups and small- and medium-sized enterprises. They are also an area of strategic and economic competition, with the PRC in particular pushing top-down approaches to standards development process and using its economic influence to compel support for its standard proposals. In May 2023, the Biden-Harris White House published the first ever U.S. Government National Standards Strategy for Critical and Emerging Technology (USG NSSCET). As outlined in the USG NSSCET, the United States will work with allies, partners, the private sector, and civil society to ensure that international standards development embraces transparency, openness, impartiality and consensus, effectiveness and relevance, coherence, and broad multistakeholder participation. The Department of State, in cooperation with the Department of Commerce and other agencies, is building enhanced capacity to engage directly in international standards development organizations and to coordinate with industry and civil society to ensure robust participation by U.S. stakeholders in standards making processes. Working with the FCC, NIST, National Telecommunications and Information Administration (NTIA), and other federal agencies, the Department of State supports standards development processes for a wide range of critical and emerging technologies and platforms, including IoT, energy grids, smart cities, and connected vehicles. The United States will continue to promote and leverage cybersecurity and privacy standards and guidelines developed by NIST through open processes with a strong connection to international standards. This approach reinforces the U.S. policy for standards: a private-sector led, industry-driven approach with government participation that emphasizes the use of international standards developed in open, transparent, and consensus-based processes. This alignment helps stakeholders reduce the burden of international regulatory and legal regimes, leading to a reduced cost of operation and a greater understanding of international policies. It also highlights the value of a bottom-up approach for other governments as they develop their cybersecurity priorities. The U.S. government has developed formal and informal methods of information sharing and standards development monitoring through regular engagement with partners and allies. Quad partners and members of the TTC, for example, have signed memoranda of cooperation to enable increased information sharing, coordination, and influence in international standards development. The Department of State has also supported increasing participation in standards development organizations from historically underrepresented nations. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State

U.S. Debuts National Standards Strategy, Prioritizes Transparent International Standards Development

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References OBJECTIVES AND RESEARCH QUESTIONS Our study addresses the following research questions: 1. Do top results from alternative search engines differ from Google's in regard to the number of unique sources? 2. Do top results from alternative search engines differ from Google's in regard to top sources? 3. Do top results from alternative search engines differ from Google's in regard to source concentration, i.e., are results distributed over more or fewer sources in different search engines? To answer these questions, we selected three alternative search engines to compare to Google. Aside from Bing, which is the biggest competitor to Google, we chose DuckDuckGo and Metager. DuckDuckGo uses results from Bing, Yahoo, and Yandex, and has the added benefit of advanced privacy settings such as non-personalized results. Metager, a German meta-search engine, aggregates results from several search engines, including Bing and Yandex. As such, either search engine might provide a more varied set of sources. Since the alternative search engines each have an index different from Google's, the comparison will give insights into what domains are favored by Google and which domains are excluded. Also, we will gain insights into the differences in ranking between them. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References OBJECTIVES AND RESEARCH QUESTIONS Our study addresses the following research questions: 1. Do top results from alternative search engines differ from Google's in regard to the number of unique sources? 2. Do top results from alternative search engines differ from Google's in regard to top sources? 3. Do top results from alternative search engines differ from Google's in regard to source concentration, i.e., are results distributed over more or fewer sources in different search engines? To answer these questions, we selected three alternative search engines to compare to Google. Aside from Bing, which is the biggest competitor to Google, we chose DuckDuckGo and Metager. DuckDuckGo uses results from Bing, Yahoo, and Yandex, and has the added benefit of advanced privacy settings such as non-personalized results. Metager, a German meta-search engine, aggregates results from several search engines, including Bing and Yandex. As such, either search engine might provide a more varied set of sources. Since the alternative search engines each have an index different from Google's, the comparison will give insights into what domains are favored by Google and which domains are excluded. Also, we will gain insights into the differences in ranking between them. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How Different Search Engines Display and Prioritize Information

In the digital age, where users expect lightning-fast loading times and seamless browsing experiences, optimizing web performance is paramount for businesses and developers alike. One key aspect of this optimization lies in efficient resource fetching and prioritization. In this article, we delve into various techniques and strategies, including pre-connect and dns-prefetch, fetchpriority, prefetch, and preload, as well as the utilization of async and defer attributes in JavaScript execution. By implementing these approaches, developers can significantly enhance page load times, improve user experience, and ultimately, bolster key performance metrics such as Largest Contentful Paint (LCP) and First Contentful Paint (FCP). Let's explore how each of these methods contributes to a faster and more responsive web. Establish Early Connections With and preconnect dns-prefetch Consider adding t or resource hints to establish early connections to important third-party origins. preconnec dns-prefetch Modern browsers what connections a page will need, but they cannot reliably predict them all. We can give them a (resource) hint. try their best to anticipate informs the browser that your page intends to establish a connection to another origin, and that you'd like the process to start as soon as possible. <link rel="preconnect"> The hint is best used for only the most critical connections. preconnect For all the rest, use to save time on the first step(round trip), the DNS lookup, which usually takes around <link rel=dns-prefetch> 20–120 ms. Preconnecting is only effective for domains other than the origin domain, so you shouldn't use it for your site. Only preconnect to critical domains you will use soon because the browser closes any connection that isn't used within 10 seconds. Unnecessary preconnecting can delay other important resources, so limit the number of preconnected domains and . test the impact preconnecting makes If a page needs to make connections to many third-party domains, preconnecting all of them is counterproductive. The preconnect hint is best used for only the most critical connections. For all the rest, use <link rel=dns-prefetch> to save time on the first step, the DNS lookup, which usually takes around . 20–120 ms is slightly different from , so dns-prefetch can serve as a fallback for browsers that don't support . Browser support for dns-prefetch preconnect support preconnect Effect on Largest Contentful Paint (LCP) Using dns-prefetch and preconnect allows sites to reduce the amount of time it takes to connect to another origin. The ultimate aim is that the time to load a resource from another origin should be minimized as much as possible. Where is concerned, it is better that resources are immediately discoverable, since are crucial parts of the user experience. Largest Contentful Paint (LCP) LCP candidates Optimizing Resource Loading With Priority Hints With fetchpriority is an attribute that allows you to hint at the relative priority of a resource (high or low). Priority Hints can help optimize the Core Web Vitals. Fetchpriority You can provide a Priority Hint using the HTML attribute. You can use the attribute with , , , and tags. The attribute allows you to specify the priority for resource types such as CSS, fonts, scripts, images, and iframe when downloaded using the supported tags. fetchpriority link img script iframe The fetchpriority attribute accepts one of three values: : This is used when the resource is considered a high priority. The browser is instructed to prioritize it, given that its heuristics don't hinder this. high : This is used when the resource is considered a low priority. The browser is instructed to deprioritize it if its heuristics allow. low : This is the default value used when there is no preference. The browser decides the appropriate priority. auto <img src="lcp-image.jpg" fetchpriority="high">
<link rel="preload" href="/js/script.js" as="script" fetchpriority="low">
<script>
fetch('https://example.com/', {priority: 'low'})
	.then(data => {
		// Trigger a low priority fetch
	});
</script> The attribute is a hint and not a directive. The browser will try to respect the developer's preference. It is also possible that the browser will apply its preferences for resource priority as deemed necessary in case of conflicts. fetchpriority Effect on Largest Contentful Paint (LCP) You can specify to boost the priority of the LCP or other critical images. fetchpriority="high" <img src="lcp-image.jpg" fetchpriority="high"> Prefetch Resources to Speed Up Future Navigations With prefetch Adding to a web page tells the browser to download entire pages or some of the resources (like scripts or CSS files), that the user might need in the future. <link rel=prefetch> The hint consumes extra bytes for resources that are not immediately needed, so this technique needs to be applied thoughtfully; only prefetch resources when you are confident that users will need them. prefetch <link rel="prefetch" href="/articles/" as="document"> Effect on Largest Contentful Paint (LCP) Prefetching images can significantly lower LCP times for LCP image elements. Prefetching stylesheets can improve both FCP and LCP, as the network time to download the stylesheet will be eliminated. Since stylesheets are render-blocking, they can reduce LCP when prefetched. In cases where the subsequent page's LCP element is a CSS background image requested via the background-image property, the image will also be prefetched as a dependent resource of the prefetched stylesheet. Prefetching JavaScript will allow the processing of the prefetched script to occur much sooner than if it were required to be fetched by the network first during navigation. This can have an effect on responsiveness metrics such as and . First Input Delay (FID) Interaction to Next Paint (INP) In cases where markup is rendered on the client via JavaScript, LCP can be improved through reduced resource load delays, and client-side rendering of markup containing a page's LCP element can occur sooner. If you're using , you can apply prefetch to routes or components that are not immediately necessary but will likely be requested soon. code-splitting <link rel="prefetch" as="script" href="1.bundle.js">

import(/* webpackPrefetch: true */ 'Pages/Step1/Step2'); Preload Critical Assets to Improve the Loading Speed With preload By preloading a certain resource, you are telling the browser that you would like to fetch it sooner than the browser would otherwise discover it because you are certain that it is important for the current page. <link rel="preload" as="script" href="critical.js">

Link: </css/style.css>; rel="preload"; as="style" Supplying the attribute helps the browser set the priority of the prefetched resource according to its type, set the right headers, and determine whether the resource already exists in the cache. Accepted values for this attribute include: , , , , and . as script style font image others Pacifico font is defined in the stylesheet with a rule. The browser loads the font file only after it has finished downloading and parsing the stylesheet. @font-face Pacifico font is preloaded, so the download happens in parallel with the stylesheet. Fonts defined with @font-face rules or background images defined in CSS files aren't discovered until the browser downloads and parses those CSS files. Preloading these resources ensures they are fetched before the CSS files have been downloaded. Because browsers don't preloaded files, preloading is useful to separate fetching from execution which can improve metrics such as Time to Interactive. Preloading works best if you your JavaScript bundles and only preload critical chunks. execute split Largest Contentful Paint (LCP) Preloading has a powerful effect on when it comes to fonts and images, as both images and text nodes can be . Hero images and large runs of text that are rendered using web fonts can benefit significantly from a well-placed preload hint and should be used when there are opportunities to deliver these important bits of content to the user faster. Largest Contentful Paint (LCP) LCP candidates However, you want to be careful when it comes to preloading—and other optimizations! In particular, avoid preloading too many resources. If too many resources are prioritized, effectively none of them are. The effects of excessive preload hints will be especially detrimental to those on slower networks where bandwidth contention will be more evident. Use or to Execute the Code async defer JavaScript execution is parser blocking. This means when the browser encounters a script it must pause DOM construction, hand this over to the JavaScript engine, and allow script execution before proceeding with DOM construction. The and attributes tell the browser that it may go on parsing the HTML while loading the script in the background, and then execute the script after it loads. This way, script downloads don't block DOM construction and page rendering. The result is that the user can see the page before all scripts have finished loading. async defer <script async src="script.js">
<script defer src="script.js"> The async and defer attributes change this behavior. With , the browser downloads the script asynchronously while it continues to parse the HTML document. When the script finishes downloading, parsing is blocked while the script executes. async With , the browser downloads the script asynchronously while it continues to parse the HTML document. The script doesn't run until the parsing is complete. defer Largest Contentful Paint (LCP) and First Contentful Paint (FCP) Proper configuration of high and low-priority scripts can give more sources to the parser to render the HTML while some of the scripts are being loaded as third-party scripts will not block the parser. And the browser is able to render the page as soon as possible.

Enhancing Web Performance: Strategies for Efficient Resource Fetching and Prioritization

Running all regression tests all the time can be impractical and inefficient and represent a paradigm shift in software development, promoting collaboration, automation, and a continuous feedback loop. These practices are instrumental in meeting the demands of a dynamic and competitive market, allowing organizations to deliver high-quality software quickly and efficiently. DevOps continuous deployment In this context, has undergone a transformation to align with the accelerated pace of development methodologies, emphasizing the roles of test automation and continuous testing practices. However, with the increasing complexity of applications, the number of automated tests tends to grow exponentially. This growth results in prolonged execution times even for automated tests, sometimes spanning hours or days, contradicting modern development practices that prioritize quick feedback loops. software testing This presents a unique challenge for testing teams: the necessity to due to resource constraints and time limitations. The sheer volume of tests can lead to a tipping point where it becomes impractical to execute every test in every regression cycle. strategically select and prioritize tests The team is then confronted with the critical task of , employing some type of prioritization criteria, and continually maintaining and updating the test suite to ensure relevance and effectiveness within the context of the adopted criteria. identifying and focusing on high-priority test cases Strategies for selecting and prioritizing test cases Considering the trade-offs between and more focused , the crucial challenge is to methodically select and prioritize test cases within the test suite, aiming to strike a delicate balance between comprehensive coverage and the effectiveness of the testing process while being mindful of resource limitations. comprehensive automated regression testing partial regression In the list below, you'll discover various methods to prioritize test cases, all intended to strike a balance between comprehensive coverage and reducing execution time. Keep in mind that this list is not exhaustive: In this article, our goal is to examine . By merging these approaches, testing teams can devise a prioritized test plan, optimizing the efficacy of their testing efforts and enhancing the overall quality of the application under test. how the integration of business impact and frequency of use methods can lead to a more comprehensive approach to test prioritization Combining the business impact and frequency of use to prioritize test cases Often, teams find themselves struggling with the challenge of quantifying the business impact and frequency of use, resorting to an intuitive and subjective approach. To elevate confidence levels, it becomes imperative for teams to transition towards a more formalized methodology. This involves the that integrates both the business impact and frequency of use for the application's functionality. This formalized approach empowers teams to prioritize test cases more effectively based on these crucial factors, minimizing reliance on guesswork. development of a systematic scoring system Here's a step-by-step guide to prioritize tests using these factors: Review the business requirements and features of your application or system. Identify those with the most substantial impact on business operations, customer satisfaction, or revenue. Identify critical business requirements and features: Analyze usage data or gather stakeholder feedback to pinpoint the most frequently used features or functionalities in production. Assess frequency of use: Develop a matrix that blends both business impact and frequency of use factors. Assign weights or scores based on their relative importance to your company. Create a prioritization matrix: Map your test cases to the corresponding requirements or features. Calculate a priority score for each test case by combining the scores or weights assigned to the business impact and frequency of use factors. Prioritize test cases: By following this approach, you can focus your testing efforts on the most business-critical and frequently used aspects of your application or system while ensuring a well-rounded test coverage. Gravity: Applying AI for test case prioritization introduces an test case weighting & scoring engine that helps optimize existing test suites by prioritizing test cases based on the business impact and frequency of use in the tested pages and flows covered by these test cases. This enables a , focusing test coverage on high-impact areas that directly affect the end-user experience. Gravity AI-powered data-driven test case prioritization It seamlessly correlates multiple dimensions, including test coverage, business impact, and frequency of use. This enables the generation of easily understandable reports and insights, empowering testing teams to prioritize tests based on concrete data rather than relying on guesswork. Gravity's ability to by processing the ingested data through machine learning algorithms. This involves translating raw data into meaningful insights using techniques such as pattern recognition, trend and correlation analysis, anomaly and outlier detection, and more. monitor usage within the production and testing environments allows it to generate comprehensive quality analytics It highlights information that allows testing teams to , pinpoint features that may be either over-tested or under-tested, and recognize redundant testing efforts in less critical areas. identify gaps in coverage If you want to learn more about Gravity, you can book a demo here: Book a demo Conclusion Embracing a focused and selective is crucial for optimizing testing resources, accelerating time-to-market, and elevating overall software quality. By directing efforts toward high-risk areas of the application, testing teams can ensure the early discovery of critical issues. test case prioritization approach Additionally, the adoption of a formalized scoring system for provides a considerable benefit for testing teams. This systematic method represents a significant improvement over relying solely on subjective opinions and the experiences of others. By employing a more , testing teams can significantly enhance their testing practices, ensuring a more rigorous and effective approach to identifying and addressing critical issues. prioritizing relevant test cases objective and data-driven methodology Author: Cristiano Caetano Head of Growth at Smartesting Software testing authority with two decades of expertise in the field. Brazilian native who has called London home for the past six years. I am the proud founder of Zephyr Scale, the leading Test Management application in the Atlassian ecosystem. Over the last ten years, my role has been pivotal in guiding testing companies to build and launch innovative testing tools into the market. Currently, I hold the position of Head of Growth at Smartesting, a testing company committed to the development of AI-powered testing tools.

Turbocharge Regression Testing: Prioritizing Test Cases Like a Pro with AI

টার্বোচার্জ রিগ্রেশন টেস্টিং: এআই সহ প্রো-এর মতো পরীক্ষার ক্ষেত্রে অগ্রাধিকার দেওয়া

Turbocharge-Regressionstests: Testfälle priorisieren wie ein Profi mit KI

Pruebas de regresión de turbocompresor: priorizar casos de prueba como un profesional con IA

Tests de régression Turbocharge : prioriser les cas de test comme un pro avec l'IA

टर्बोचार्ज रिग्रेशन परीक्षण: एआई के साथ एक पेशेवर की तरह परीक्षण मामलों को प्राथमिकता देना

ターボチャージ回帰テスト: AI を使用してプロのようにテスト ケースに優先順位を付ける

터보차지 회귀 테스트: AI를 사용해 전문가처럼 테스트 사례 우선순위 지정

Teste de regressão turbocharge: priorizando casos de teste como um profissional com IA

Регрессионное тестирование с турбонаддувом: расстановка приоритетов тестовых случаев с помощью искусственного интеллекта как профессионал

Turboşarj Regresyon Testi: Yapay Zeka ile Bir Profesyonel Gibi Test Durumlarına Öncelik Verme

Thử nghiệm hồi quy tăng áp: Ưu tiên các trường hợp thử nghiệm như một chuyên gia với AI

涡轮增压回归测试：像人工智能专家一样确定测试用例的优先级

Remarks of Principal Deputy U.S. Chief Technology Officer Deirdre K. Mulligan at the Public Interest Technology University Network Convening Thank you for having me here today. It’s great to be back with this group. I’ve spent my career in the public interest technology field. While I’m a lawyer by training, throughout my career in civil society, academia, and now in the federal government, I’ve collaborated with technologists, computer and data scientists, and social scientists who consider both the design and regulation of technical systems as essential to protecting democratic values and human rights, as well as our safety and security. Today, I have the privilege of serving as Principal Deputy U.S. Chief Technology Officer in the White House Office of Science and Technology Policy, or OSTP. At OSTP, a team of folks who embody the ethos of public interest technology work to maximize the benefits of science and technology to advance health, prosperity, security, environmental quality, and justice for all Americans. We carry out this mission by advising the President and White House senior staff on key issues related to science and technology, and by coordinating federal government technology policy and priorities. Our nation has immense aspirations: to achieve robust health and ample opportunity for each person in every community; to overcome the climate crisis by reimagining our infrastructure, restoring our relationship with nature, and securing environmental justice; to sustain global security and stability; to build a competitive economy that creates good-paying jobs; and to foster a strong, resilient, and thriving democracy. Technology plays a vital role in achieving each of these goals. Today, I’d like to do three things. First, I’ll tell you a bit about what the Biden-Harris Administration is doing to ensure AI and other technology is developed and implemented in ways that support democratic values and human rights as well as the public’s safety and security. Second, I’ll describe how we’re making it easier for members of the public interest technology field to serve in the federal government. And third, I’ll set out the vital role that universities play in shaping the future of the public interest technology field in government. President Biden and all of us in the Biden-Harris Administration are committed to using technology and data to support the public interest: ensuring technology works for every member of the public, protects our safety and security, and reflects and protects democratic values and human rights. While you might not read executive orders and federal regulations word for word—if you do, you’ll see this commitment threaded throughout the Administration’s actions. For example, on his first day in office, the President signed an executive order establishing a working group on equitable data focused on using data to evaluate and align federal programs and policies to ensure they are serving all Americans. In December 2021, the President signed an executive order directing the federal government to modernize and improve service delivery, and specifically, to leverage technology solutions to do so. In May 2022, the President signed an executive order directing the federal government to assess how data can inform equitable and effective policing, as well as identify privacy, civil rights, and civil liberties concerns related to the use of technology, including facial recognition and predictive algorithms. And this summer, the Department of Justice proposed a new rule that would establish accessibility standards for state and local governments’ web and mobile app-based services, which would help advance equal access to key programs and services for people with disabilities. This is only a sampling of key activities we are taking to make sure technology works for all Americans and supports democratic values and institutions. And then, of course, there is the Administration’s efforts to ensure we can use AI to tackle society’s most challenging problems and do so in a way that protects safety, security, equity, and civil rights. AI is one of the most powerful technologies of our times, and AI systems are increasingly touching every aspect of our lives. These systems have brought benefits across a wide range of domains: from detecting cancer earlier to improving agricultural yield to helping small business owners cut costs. Advances in AI can supercharge progress, making life better for everyone and helping to meet our greatest challenges. At the same time, as President Biden has said, to seize the benefits of AI, we must first manage its risks. It seems like every day we read another story, or another study, or hear from another person whose life has been negatively impacted by AI: high-quality images and text are being used to deceive consumers; more and more sensitive information is being used to train AI systems like facial recognition, which is further eroding privacy; and the harms of AI are being disproportionately felt by already underserved communities, such as how AI is being used to create and distribute non-consensual intimate images of women and girls. These systems are having dramatic impacts on Americans’ lives—putting our rights, our safety, and our security at risk. Since the start, this Administration has been working to ensure that AI is developed and used to bolster the public interest. In order to do that, we have been clear about our values and how they should be embedded into the systems that are influencing our daily lives. Just over a year ago—after hearing from hundreds of people across the United States and beyond—OSTP released the as a guide for how to leverage AI technologies in ways that reinforce our highest values, and help protect society from AI’s risks. Blueprint for an AI Bill of Rights Since then, President Biden issued an executive order directing agencies to root out bias in AI and to seek opportunities to involve civil rights experts within government in decisions about technology. The President, Vice President, and senior Administration leaders have continued meeting with experts, researchers, and civil society leaders to discuss risks from AI. The White House announced that leading AI companies would provide their AI systems for the first-ever public “red teaming” assessment, which was held at the DEF CON cybersecurity conference and included assessments of these AI systems against the broad range of risks outlined in the Blueprint for an AI Bill of Rights. This summer, President Biden secured voluntary commitments from 15 leading AI companies to follow a series of practices to limit risks to safety, security, and trust—helping to create a bridge to regulation for this fast-moving technology. And we’re not done yet. We are currently developing an executive order on AI and we will pursue bipartisan legislation to help America lead the way in responsible innovation in AI. The Office of Management and Budget will issue guidance for how federal agencies can leverage AI responsibly. The U.S. government has to lead by example—to harness the benefits of AI for the American public, we must establish what it means to responsibly and develop tech, and we must show how it can be done. Our ability to meet this moment depends on you. The federal government—all branches and levels of government—need the folks in this room. We can’t use AI to benefit the public, or regulate the AI marketplace to protect against risks without people with your talent and passion. That’s why we’re making it easier for the students and academics in this room to join us. The Office of Personnel Management is making it easier for agencies to quickly hire technical talent such as data scientists. Programs stood up in the Obama-Biden Administration like the Presidential Innovation Fellowship and the U.S. Digital Service continue to bring technical talent into the federal government. And programs launched by the Biden-Harris Administration, such as the U.S. Digital Corps, are now bringing on early-career technologists—something that might be of particular interest to the PIT-UN community. And there are also external fellowships that place technologists in federal agencies and the halls of Congress. Additionally, with the unprecedented investments coming into American communities through the Investing in America agenda, now is a great time for public interest technologists to serve in state and local government and local nonprofits to help deliver to those who need it the most. And as we recruit, we’re looking for individuals with the mix of technical and policy expertise that the public interest tech field cultivates. We need interdisciplinary teams that can apply AI to key problems and grapple with the societal impacts of AI. As you all know, we need technologists, social scientists, lawyers, ethicists, and others to build AI and to design the tools, methods, and regulations to ensure AI embeds our values and protects our rights. I thought sharing the Technology team’s mission statement would give you a strong sense of our alignment with this movement and how wide-open OSTP’s doors are to you, your ideas, and your work. We strive for: A world that uses technology and data to advance and protect civil rights and civil liberties, equity and accessibility, and democratic values along with economic prosperity, scientific progress, and national security. A world in which technologists, data scientists, and social scientists are partners in establishing and implementing public policy. It will take all of us to get this right. This brings me to the last point I want to stress today. Universities have a vital role to play in advancing public interest technology. You all know that—that’s why you’re here today. But I want to drill down on two specific ways that you can keep making progress here. First, you can create the multidisciplinary opportunities that allow students and faculty to grapple with these challenging problems in all their real-world messiness. From academic centers that cross multiple disciplines, to classes that bring together perspectives from different backgrounds, this group is already pushing the frontiers of interdisciplinary and experiential learning. From cybersecurity clinics, to interdisciplinary colloquium and courses, to capstone experiences in local government, these opportunities are necessary to cultivate the knowledge and skills of the next generation of public interest tech field. I urge you all to do more of this type of work. Second, you all can make it possible for folks from all walks of life to join the public interest technology field. Universities can do more to support faculty, students, and graduates joining the public interest technology field. Support can take many forms, including funding to support faculty stints of government service that bring needed expertise into government and bring public service and public value-oriented questions back into academic research; loan forgiveness and fellowships that ensure diverse graduates can pursue careers in government; externships and internships, co-ops, capstone programs, and clinics that expose students to public interest tech career opportunities, and expose all levels of government to the value of having members of the public interest field in their shops. My team and I are happy to chat with anyone who has started a program or is contemplating one that would grow the public interest tech field in government. And we’re happy to chat with those who are looking to grow the capacity needed in civil society organizations as well—these organizations also need your talent, and will benefit from loan forgiveness, fellowships, and tours of service. You all are vital partners in this work. The Administration looks forward to working with you and with students and academics across the country to leverage technology to create a better future for us all. We are at a pivotal moment, where everyone around the globe recognizes the importance of not only regulating but also proactively shaping and building and using technology to support our values. There could not be a better time to work in public interest technology. I look forward to working with you all to build the vision and workforce to seize the opportunities and manage the risks of AI. This content was originally published on October 13, 2023 in whitehouse.gov.

Deputy CTO Deirdre K. Mulligan Discusses Biden Administration's Tech Priorities

As many professionals reflect upon their workflow and try to identify opportunities for enhancement, it becomes more and more obvious that in all processes, including product management, prioritization can be the growth point. Poor prioritization can manifest in seemingly minor inconveniences, such as attempting to fit an excessive amount of work into a constrained timeframe – a predicament familiar to us all. Nevertheless, when these challenges persist consistently, the cumulative effect transforms these minor stresses into significant obstacles, resulting in the creation of subpar products and an immense waste of valuable time. The question then arises: how can one address prioritization challenges proactively, preventing them from evolving into significant hurdles? In this article, we'll explore proven prioritization techniques that can help product managers and teams make informed decisions. Each method offers unique insights into product development. The MoSCoW method sorts features into must-haves and nice-to-haves. The Kano Model focuses on customer satisfaction. The Eisenhower Matrix balances urgency and importance. These methods together form a toolkit helping product teams handle challenges, allocate resources wisely, and create products that connect with their audience. 1.MoSCoW Method The MoSCoW method was proposed by of Oracle UK Consulting in the mid-1990s. It is an efficient way to sort tasks into critical and non-critical categories. Dai Clegg The MoSCoW acronym stands for: These are the essential features or requirements without which the project or product would be considered a failure. They represent the core functionality that must be delivered for the project to be successful. Must-haves (M): These are important features that are not critical for the project's success but would significantly enhance its value. They are prioritized after addressing the must-haves. Should-haves (S): These are nice-to-have features that are desirable but not necessary for the project's success. They are typically considered after addressing must-haves and should-haves. Could-haves (C): These tasks are desirable (for example, "Would like to have…") but aren't included in this project. You can also use this category for the least critical activities. Won't-haves or Would-haves (W): Example of the MoSCoW Method Imagine we are developing a new e-commerce website, so we will have the following prioritization list: ecure online payment system, product listing and search functionality, user registration, and authentication. Must-haves (M): S Customer reviews and ratings, integration with social media for sharing products, wishlist functionality. Should-haves (S): Loyalty program for frequent customers, real-time customer support chat, advanced search filters. Could-haves (C): Augmented reality features for virtual product try-on (considered too complex for the initial release), cryptocurrency payment options (considered a low priority at the moment). Won't-haves (W): By categorizing features into these four groups, the project team can ensure that they focus on the most critical elements first. We also get a clear understanding of what must be delivered and what can be considered as additional enhancements. This helps in managing stakeholder expectations and resources effectively throughout the project lifecycle. Additionally, the MoSCoW method allows for flexibility in adapting to changing project requirements and priorities. 2. The Kano Model The Kano Model, developed by , is a framework for understanding and prioritizing customer needs and expectations in product development. It categorizes features into three main types: The model helps product teams identify which features will lead to increased customer satisfaction. Professor Noriaki Kano Basic Needs, Performance Needs, and Excitement Needs. Basic needs are fundamental requirements that, when met, do not necessarily increase customer satisfaction but can cause dissatisfaction if not fulfilled. Basic Needs: Performance needs are features where the level of satisfaction is directly proportional to the degree of fulfillment. Meeting or exceeding expectations in these areas can positively impact satisfaction. Performance Needs: Excitement needs are unexpected or innovative features that can significantly boost customer satisfaction when present but don't cause dissatisfaction if absent since customers don't expect them. Excitement Needs (Delighters): Example of the Kano Model This time let us take the process of a Fitness App development. Our is to accurately track steps and calories burned. Users expect the app to provide reliable data; if it consistently fails to do so, dissatisfaction will occur. Basic Need The will be a personalized workout recommendation based on user preferences and goals. As the app tailors workout suggestions more accurately, user satisfaction increases. Performance Need The last one is the — gamification features. It can be virtual rewards or challenges. These elements aren't necessary for the core functionality, but they can significantly enhance the user experience and satisfaction when present. Excitement need After completing the five-step method and having a prioritized list of features, you can enhance your planning by creating a benefit and cost model for each feature. This step allows you to identify features that, if implemented, could yield a substantial return on investment through increased sales. This insight becomes particularly valuable in guiding your product development cycle, especially when facing constraints in time and financial resources. 3. The Eisenhower Matrix The Eisenhower Matrix, also known as the Urgent-Important Matrix, is a productivity and prioritization tool named after former U.S. President Dwight D. Eisenhower, who famously said, "I have two kinds of problems: the urgent and the important. The urgent are not important, and the important are never urgent." The matrix categorizes tasks into four quadrants based on their urgency and importance: Tasks that are both urgent and important are given the highest priority and should be tackled immediately. These are critical tasks that have a significant impact on the project or product. Urgent and Important (Do First): Tasks in this quadrant are important for long-term goals but are not time-sensitive. They should be scheduled and planned so that they do not tend to become urgent in the future. Important, Not Urgent (Schedule): Tasks that are urgent but not important can be delegated to others if possible. They may not directly contribute to the project's success but still need attention. Urgent, Not Important (Delegate): Tasks in this quadrant are neither urgent nor important and can be postponed or eliminated. They do not contribute significantly to the project's success. Not Urgent and Not Important (Eliminate or Postpone): Example of the Eisenhower Matrix Let's say a product manager is working on software for a customer relationship management (CRM) system. Here's how they might apply the Eisenhower Matrix to prioritize product requirements: The CRM system has a critical security vulnerability that needs immediate attention to protect customer data. This becomes the top priority for the development team. Urgent and Important (Do First): Enhancing the user interface for a more intuitive user experience is important for customer satisfaction but not immediately urgent. The product manager schedules this task for the next development sprint. Important, Not Urgent (Schedule): A minor bug in a less critical feature has been identified. The product manager delegates this task to a junior developer while they focus on more critical issues. Urgent, Not Important (Delegate): A proposed feature that would add a decorative element to the user interface but doesn't contribute significantly to functionality is deemed not urgent and not important. Not Urgent and Not Important (Eliminate or Postpone): The product manager decides to postpone this feature until more critical tasks are addressed. As can be seen, the Eisenhower Matrix is a simple tool for considering the long-term outcomes of your tasks and focusing on what will make the project most effective. It helps to visualize all the acute tasks in a matrix of urgent/important and provides that all tasks of a bigger project will fall into one of the above-mentioned four quadrants. RICE Method The RICE scoring method is a prioritization framework used to rank and prioritize features or tasks based on their potential impact. The acronym "RICE" stands for Each of these factors is assigned a numerical score, and the total score is used to determine the priority of a particular item. Reach, Impact, Confidence, and Effort. Here's a breakdown of each component of the RICE method: Reach measures the number of users or customers who will be affected by the feature or task. It quantifies the potential audience impacted by the implementation. Reach: Assign a numerical value from 1 to 10, with 1 being a low reach and 10 being a high reach. For example, if a feature affects all users, it might receive a score of 10. Scoring: Impact assesses the potential positive effect or benefit of implementing the feature or task. It quantifies the level of positive change or improvement. Impact: Similar to reach, assign a numerical value from 1 to 10. A feature that significantly improves user engagement or key performance indicators might receive a high impact score. Scoring: Confidence reflects the level of certainty or confidence the team has in the estimates for reach, impact, and effort. It accounts for the level of uncertainty associated with the prioritization. Confidence: Assign a percentage score from 1% to 100%. A feature with high confidence in the estimates might receive a score of 90%, while a more uncertain estimate might get a lower score, such as 50%. Scoring: Effort measures the resources, time, and complexity required to implement the feature or task. It quantifies the cost or effort associated with the implementation. Effort: Similar to reach and impact, assign a numerical value from 1 to 10, with 1 being low effort and 10 being high effort. A straightforward task might receive a low effort score, while a complex feature might receive a high effort score. Scoring: The RICE score is calculated using the formula: (Reach x Impact x Confidence) / Effort. Example of the RICE Method: Now, we will try to prioritize features for an imaginable Mobile App using the RICE method. Let us compare Feature A and Feature B. Conclusion Each method in this compilation serves a common purpose: guaranteeing that your focus is consistently on the most crucial tasks. Ultimately, the specific technique chosen becomes secondary. Whether you opt for a single method, combine various techniques, or even customize your approach by integrating aspects from different methods, the key is to choose an approach that resonates with your workflow and fits your product the most. The paramount factor is taking action and initiating the work based on a method that aligns with your team's preferences and work style.

Mastering Prioritization: Strategies for Effective Product Management

The United States v Meta Platforms Court Filing October 24, 2023 is part of . You can jump to any part in this filing . This is part 88 of 100. HackerNoon’s Legal PDF Series here COUNT XLIII: VIOLATIONS OF OHIO CONSUMER SALES PRACTICES ACT – UNCONSCIONABLE CONSUMER SALES ACTS OR PRACTICES - OHIO REVISED CODE §1345.03 1101. Ohio realleges and incorporates herein by reference each of the allegations contained in the preceding paragraphs 1 through 850 as though fully alleged in this cause of action. 1102. Meta knowingly designed platforms that ignored the damaging effect said platforms have on young users’ psychological vulnerabilities. Meta made immoral, unethical, oppressive and unscrupulous decisions that prioritized Meta’s financial gain at the expense of its young users’ mental health. 1103. Meta’s conduct, acts or omissions, as described herein, constitute unconscionable acts and practices in violation of the CSPA, O.R.C. 1345.03(A). 1104. Each unconscionable act or practice engaged in by Meta as recited above constitutes a separate violation of the CSPA. 1105. The acts or practices described above have been previously determined by Ohio courts to violate the CSPA, Ohio Rev. Code §1345.01, et seq. Meta committed said violations after such decisions were made available for public inspection pursuant to Ohio Rev. Code §1345.05(A)(3). Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:23-cv-05448 retrieved on October 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Washingtonpost.com

Ohio Blasts Meta for Prioritizing Financial Gain Over Young Users' Mental Health 

is an API Gateway, which builds upon the reverse-proxy to offer a plugin-based architecture. The main benefit of such an architecture is that it brings structure to the configuration of routes. It’s a help at scale, when managing hundreds or thousands of routes. Apache APISIX OpenResty In this post, I’d like to describe how plugins, priority, and phases play together and what pitfalls you must be aware of. APISIX plugin’s priority When you configure a route with multiple plugins, Apache APISIX needs to execute them in a order so that the results are the same over time. For this reason, every APISIX plugin has a . You can check a plugin priority directly in the code. For example, here’s the relevant code fragment for the plugin: consistent hardcoded priority basic-auth local _M = {
    version = 0.1,
    priority = 2520,                                 #1
    type = 'auth',
    name = plugin_name,
    schema = schema,
    consumer_schema = consumer_schema
} Priority is 2520 For documentation purposes, the file lists the priority of all out-of-the-box plugins. Here’s an excerpt: default-config.yaml plugins:
  - ldap-auth                      # priority: 2540
  - hmac-auth                      # priority: 2530
  - basic-auth                     # priority: 2520
  - jwt-auth                       # priority: 2510
  - key-auth                       # priority: 2500
  - consumer-restriction           # priority: 2400
  - forward-auth                   # priority: 2002
  - opa                            # priority: 2001
  - authz-keycloak                 # priority: 2000
  #- error-log-logger              # priority: 1091
  - proxy-cache                    # priority: 1085
  - body-transformer               # priority: 1080
  - proxy-mirror                   # priority: 1010
  - proxy-rewrite                  # priority: 1008
  - workflow                        # priority: 1006
  - api-breaker                    # priority: 1005
  - limit-conn                     # priority: 1003
  - limit-count                    # priority: 1002
  - limit-req                      # priority: 1001 Imagine a route configured with and . Because of their respective priority, would run before . proxy-mirror proxy-rewrite proxy-mirror proxy-rewrite upstream:
  - id: 1
    nodes:
      "oldapi:8081": 1

routes:
  - id: 1
    uri: "/v1/hello*"
    upstream_id: 1
    plugins:
      proxy-rewrite:                                 #1-3
        regex_uri: ["/v1(.*)", "$1"]
      proxy-mirror:                                  #2
        host: http://new.api:8082 The plugin ordering in this file is not relevant has priority 1010 and will execute first proxy-mirror has priority 1008 and will run second proxy-rewrite The above setup has an issue. For example, if we call , APISIX will first mirror the request, and then remove the prefix. Hence, the new API receives instead of . It’s possible to override the default priority to fix it: localhost:9080/v1/hello /v1 /v1/hello /hello routes:
  - id: 1
    uri: "/v1/hello*"
    upstream_id: 1
    plugins:
      proxy-rewrite:
        _meta:
          priority: 1020                             #1
        regex_uri: ["/v1(.*)", "$1"]
      proxy-mirror:
        host: http://new.api:8082 Override the default priority Now, has higher priority than : the former runs before the latter. proxy-rewrite proxy-mirror In this case, it works flawlessly; in others, it might not. Let’s dive further. APISIX phases APISIX runs plugins not only according to their priorities but also through dedicated phases. Because APISIX builds upon OpenResty, which builds upon ngxinx, the phases are very similar to the phases of these two components. nginx defines several an HTTP request goes through: phases NGX_HTTP_POST_READ_PHASE NGX_HTTP_SERVER_REWRITE_PHASE NGX_HTTP_FIND_CONFIG_PHASE NGX_HTTP_REWRITE_PHASE NGX_HTTP_POST_REWRITE_PHASE NGX_HTTP_PREACCESS_PHASE NGX_HTTP_ACCESS_PHASE NGX_HTTP_POST_ACCESS_PHASE NGX_HTTP_PRECONTENT_PHASE NGX_HTTP_CONTENT_PHASE NGX_HTTP_LOG_PHASE Each phase focuses on a task, , verifies that the client is authorized to make the request. i.e. NGX_HTTP_ACCESS_PHASE In turn, OpenResty offers similarly named phases. From nginx documentation: Finally, here are the phases of Apache APISIX: rewrite access before_proxy header_filter body_filter log We have seen two ways to order plugins: and Now comes the most important rule: ! by priority by phase. order by priority only works inside a phase For example, the plugin has a priority of 900 and runs in phase ; the plugin has a priority of 995 and runs in phase . Regardless of their respective priorities, will happen before , because happens before . Likewise, changing a priority won’t "move" a plugin out of its phase. redirect rewrite gzip body_filter redirect gzip rewrite body_filter The example above with and worked because both run in the phase. proxy-mirror proxy-rewrite rewrite The main issue is that priority is documented in the file, while the phase is buried in the code. Worse, some plugins run across different phases. For example, let’s check the proxy plugin and, more precisely, the functions defined there: config-default.yaml proxy-rewrite local function is_new_headers_conf(headers) local function check_set_headers(headers) function _M.check_schema(conf) function _M.rewrite(conf, ctx) The name of the function is suspiciously similar to one of the phases above. Looking at other plugins, we see the same pattern repeat. Apache APISIX runs plugin functions with the same name as the phase. rewrite() I took the liberty of summarizing all plugins and their respective phases in a table. +----------------------+---------+--------+-------------+--------------+------------+-----+
| Plugin               | Phase                                                            |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| General              | rewrite | access | beforeproxy | headerfilter | bodyfilter | log |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| redirect             | X       |        |             |              |            |     |
| echo                 |         |        |             | X            | X          |     |
| gzip                 |         |        |             | X            |            |     |
| real-ip              | X       |        |             |              |            |     |
| ext-plugin-pre-req   | X       |        |             |              |            |     |
| ext-plugin-post-req  |         | X      |             |              |            |     |
| ext-plugin-post-resp |         |        | X           |              |            |     |
| workflow             |         | X      |             |              |            |     |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| Transformation       | rewrite | access | beforeproxy | headerfilter | bodyfilter | log |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| response-rewrite     |         |        |             | X            | X          |     |
| proxy-rewrite        | X       |        |             |              |            |     |
| grpc-transcode       |         | X      |             | X            | X          |     |
| grpc-web             |         | X      |             | X            | X          |     |
| fault-injection      | X       |        |             |              |            |     |
| mocking              |         | X      |             |              |            |     |
| degraphql            |         | X      |             |              |            |     |
| body-transformer     | X       |        |             | X            | X          |     |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| Authentication       | rewrite | access | beforeproxy | headerfilter | bodyfilter | log |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| key-auth             | X       |        |             |              |            |     |
| jwt-auth             | X       |        |             |              |            |     |
| basic-auth           | X       |        |             |              |            |     |
| authz-keycloak       |         | X      |             |              |            |     |
| authz-casdoor        |         | X      |             |              |            |     |
| wolf-rbac            | X       |        |             |              |            |     |
| openid-connect       | X       |        |             |              |            |     |
| cas-auth             |         | X      |             |              |            |     |
| hmac-auth            | X       |        |             |              |            |     |
| authz-casbin         | X       |        |             |              |            |     |
| ldap-auth            | X       |        |             |              |            |     |
| opa                  |         | X      |             |              |            |     |
| forward-auth         |         | X      |             |              |            |     |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| Security             | rewrite | access | beforeproxy | headerfilter | bodyfilter | log |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| cors                 | X       |        |             | X            |            |     |
| uri-blocker          | X       |        |             |              |            |     |
| ua-restriction       |         | X      |             |              |            |     |
| referer-restriction  |         | X      |             |              |            |     |
| consumer-restriction |         | X      |             |              |            |     |
| csrf                 |         | X      |             | X            |            |     |
| public-api           |         | X      |             |              |            |     |
| chaitin-waf          |         | X      |             |              |            |     |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| Traffic              | rewrite | access | beforeproxy | headerfilter | bodyfilter | log |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| limit-req            |         | X      |             |              |            |     |
| limit-conn           |         | X      |             |              |            | X   |
| limit-count          |         | X      |             |              |            |     |
| proxy-cache (init)   |         | X      |             | X            | X          |     |
| proxy-cache (disk)   |         | X      |             | X            |            |     |
| proxy-cache (memory) |         | X      |             | X            | X          |     |
| request-validation   | X       |        |             |              |            |     |
| proxy-mirror         | X       |        |             |              |            |     |
| api-breaker          |         | X      |             |              |            | X   |
| traffic-split        |         | X      |             |              |            |     |
| request-id           | X       |        |             | X            |            |     |
| proxy-control        | X       |        |             |              |            |     |
| client-control       | X       |        |             |              |            |     |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| Observability        | rewrite | access | beforeproxy | headerfilter | bodyfilter | log |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| zipkin               | X       | X      |             | X            |            | X   |
| skywalking           | X       |        |             |              |            | X   |
| opentelemetry        | X       |        |             |              |            | X   |
| prometheus           |         |        |             |              |            |     |
| node-status          |         |        |             |              |            |     |
| datadog              |         |        |             |              |            | X   |
| http-logger          |         |        |             |              | X          | X   |
| skywalking-logger    |         |        |             |              |            | X   |
| tcp-logger           |         |        |             |              |            | X   |
| kafka-logger         |         |        |             |              | X          | X   |
| rocketmq-logger      |         |        |             |              | X          | X   |
| udp-logger           |         |        |             |              |            | X   |
| clickhouse-logger    |         |        |             |              | X          | X   |
| syslog               |         |        |             |              |            | X   |
| log-rotate           |         |        |             |              |            |     |
| error-log-logger     |         |        |             |              |            |     |
| sls-logger           |         |        |             |              |            | X   |
| google-cloud-logging |         |        |             |              |            | X   |
| splunk-hec-logging   |         |        |             |              |            | X   |
| file-logger          |         |        |             |              | X          | X   |
| loggly               |         |        |             |              | X          | X   |
| elasticsearch-logger |         |        |             |              |            | X   |
| tencent-cloud-cls    |         | X      |             |              | X          | X   |
| loki-logger          |         |        |             |              | X          | X   |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| Others               | rewrite | access | beforeproxy | headerfilter | bodyfilter | log |
+----------------------+---------+--------+-------------+--------------+------------+-----+
| serverless           |         | X      |             |              |            |     |
| openwhisk            |         | X      |             |              |            |     |
| dubbo-proxy          |         | X      |             |              |            |     |
| kafka-proxy          |         | X      |             |              |            |     |
+----------------------+---------+--------+-------------+--------------+------------+-----+ Conclusion I’ve detailed Apache APISIX plugin phases and priorities in this post. I’ve explained their relationship with one another. Icing on the cake, I documented each out-of-the-box plugin’s phase(s). I hope it will prove helpful. To go further: Default plugin priorities Apache APISIX plugin execution lifecycle Plugin execution code Originally published at , on December 10th, 2023 A Java Geek

Navigating Apache APISIX: A Strategic Approach to Plugin Priorities and Execution Phases

As the global startup investment slowdown begins to show signs of turning around, Southeast Asia’s startup ecosystem should prepare for a new fundraising landscape. A new report released this month by , Bain & Co, and Temasek, an investment firm owned by the government of Singapore, that investment into digital companies in Southeast Asia had reached its lowest level in six years. Google revealed Factors, including the global investment slowdown following the high-profile collapse of US-based , have played a role. In March, just after the bank’s implosion. Silicon Valley Bank Singapore Monetary Authority Chief Fintech Officer Sopnendu Mohanty , “Accessing capital will be the single biggest challenge” facing startups in the short term. said However, as the dark cloud that has hung over markets for the past year begins to of clearing, and the coffers of regional and global investors fill once more, Southeast Asia is poised as a region ripe for investment. Founders, though, should be aware of the changing fundraising landscape. show signs Founders should be laser-focused on profitability I have worked with Asian companies and governments for over a quarter century, organizing summits attended by heads of state, captains of industry, and entrepreneurs — most recently, the Horasis Asia Meeting being held in Binh Duong, Vietnam on December 3-4. My familiarity with the region gives me every confidence that Asian entrepreneurs will be able to emerge from the so-called “funding winter.” The region embraces a business culture of maximizing efficiency and profit — a mindset that will serve technology entrepreneurs courting tight-fisted investors who are increasingly worried about profitability over valuation. According to , Asian organizations have “pioneered many of the approaches that define world-class business practices today, from lean manufacturing to global business services.” McKinsey & Company Even as VC firms such as and are closing new, multi-million dollar funds for Southeast Asian startups, founders should be prepared to show a clear path to profitability and exit. During an investor conference in September, a general partner at Singapore-based Qualgro that the firm “passed one investment where founders were very good, the market was great … but we couldn’t see where the exit would be.” 500 Global Vertex told the audience Startups should keep sustainability at heart In recent years, some Southeast Asian countries have emerged as bright spots in the push toward sustainability. In 2021 Vietnam, a nation of just 97 million, became the world’s producer of solar power, and in August, Vietnamese electric vehicle (EV) company VinFast was than Ford and General Motors when it debuted on the NYSE. tenth-largest trading higher It is imperative that the next generation of startup unicorns coming out of the region keep sustainability at heart. Not only will this keep Southeast Asian countries on track to hit their goal of net-zero emissions by 2050, but it’s also likely to give them a leading edge in the current investor climate. While some startup sectors lagged during Q3 2023, late-stage companies in select industries such as semiconductors, AI, EVs and sustainability, received larger investments, according to from Crunchbase. And last year, global public and private investment into green energy hit $1 trillion for the first time, up from $32 billion in 2004, to BloombergNEF data. data according A recent of digital economy companies across Southeast Asia showed that while 85% of respondents showed interest in sustainability, less than 50% had actually implemented sustainable practices in their organizations. Startups must go beyond intention and act on sustainability practices, or risk being left behind in the global funding push towards sustainability. survey An end to the current global funding drought will come sooner than later, and Asian startups must remain poised to hit the ground running when the investment tides shift. *, Chairman, Horasis.* - Frank-Jürgen Richter Originally published on The Tech Panda.

Charting a Course: Strategic Priorities for Southeast Asian Startups in the Face of Funding Revival

Rise of Generative AI and GPU shortage Generative AI, enabled by large language models ( ) like , has caused shockwaves in the tech world. ChatGPT's meteoric rise has triggered the global tech industry to reassess and prioritize Generative AI, reshaping product strategies in real-time. LLMs GPT-4 Integration of LLMs has given product developers an easy way to incorporate AI-powered features into their products. But it's not all smooth sailing. A glaring challenge looms large for product leaders: the GPU shortage and spiraling costs. The increasing number of AI startups and services has led to high demand for high-end GPUs such as A100s and H100s, thereby overwhelming Nvidia and its manufacturing partner TSMC, both of whom are struggling to meet the supply. Online forums like Reddit are abuzz with frustrations over GPU availability, echoing the sentiment across the tech community. It's become so dire that both AWS and Azure have had no choice but to implement quota systems. This bottleneck doesn't just squeeze startups; it’s a stumbling block for tech giants like OpenAI. At a recent off-the-record meeting in London, OpenAI's CEO Sam Altman candidly acknowledged that the computer chip shortage is stymieing ChatGPT’s advancement. Altman reportedly lamented that the dearth of computing power has resulted in subpar API availability and has obstructed OpenAI from rolling out larger "context windows" for ChatGPT. Prioritizing AI Features On one hand, product leaders find themselves caught in a relentless push to innovate, facing the expectations to deliver cutting-edge features that leverage the power of Generative AI. On the other hand, they grapple with the harsh realities of GPU capacity constraints. It's a complex juggling act, where ruthless prioritization becomes not just a strategic decision but a necessity. Given that GPU availability is poised to remain a challenge for the foreseeable future, product leaders must think strategically about GPU allocation. Traditionally, product leaders have leaned on prioritization techniques like the Customer Value/Need vs. Effort Matrix. This method, however logical in a world where computational resources were abundant, now demands a bit of reevaluation. In our current paradigm, where computing is the constraint and not software talent, product leaders must redefine how they prioritize various products or features, bringing GPU limitations to the forefront of strategic decision-making. Planning around capacity constraints might seem unusual for the tech industry, but it's a commonplace strategy in other industries. The underlying concept is straightforward: the most valuable factor is the time spent on the constrained resource, and the objective is to optimize the value per unit of time spent on that constraint. As a former consultant, I've successfully applied this framework across various industries. I believe that tech product leaders can also use a similar approach to prioritize products or features while GPU constraints exist. When applying this framework, the most straightforward measure of value is profitability. However, in tech, profitability might not always be the appropriate metric, particularly when venturing into a new market or product. Thus, I have adapted the framework to align with the success metrics generally used in tech, outlining a simple four steps process: : First and foremost, identify your North Star metric. This is the contribution of each product or feature, something that encapsulates the essence of its worth. Some concrete examples might include: Contribution An increase in Revenue and Profit Gains in Market Share Growth in the Number of Daily/Monthly Active Users Gauge the number of GPUs needed for each product or feature. Focus on key factors like Number of GPUs Required: Number of Queries per User per Day Number of Daily Active Users The complexity of the Query (i.e., how many tokens each query consumes) : Break it down to the specifics. How does each GPU contribute to the overall goal? Understanding this will give you a clear picture of where your GPUs are best allocated. Calculate Contribution per GPU : Now, it's time to make the tough decisions. Rank your products by their Contribution per GPU, and then line them up accordingly. Focus on the products with the highest Contribution per GPU first, ensuring that your limited resources are channeled into the areas where they'll make the most impact. Prioritize Products Based on Contribution per GPU With GPU constraints no longer a blind spot, but a quantifiable factor in the decision-making process, your company can more strategically navigate the GPU shortage. To bring this framework to life, let's visualize a scenario where you, as a product leader, are grappling with the challenge of prioritizing among four different products: Product A Product B Product C Product D Revenue Potential (Contribution) $100M $80M $50M $25M Number of GPUs Required 1000 450 500 50 Contribution Per GPU $0.1M/GPU $0.18M/GPU $0.1M/GPU $0.5M/GPU Although Product A has the highest revenue potential, it doesn't yield the highest contribution per GPU. Surprisingly, Product D, with the least revenue potential, offers the most substantial return per GPU. By prioritizing based on this metric, you could maximize total potential revenue. Let's say you have a total of 1000 GPUs at your disposal. A straightforward choice might have you opting for Product A, generating a revenue potential of $100M. However, applying the prioritization strategy described above, you could achieve $155M in revenue: Priority Order Product Revenue Gain GPUs 1 Product D $25M 50 2 Product B $80M 450 3 Product C $50M 500 Total $155M 1000 The same method can be applied to other contribution metrics, such as market share gain: Product A Product B Product C Product D Market Share Gain (Contribution) 5% 4% 2.5% 1.25% Number of GPUs Required 1000 500 500 50 Contribution Per GPU 0.005%/GPU 0.008%/GPU 0.005%/GPU 0.025%/GPU Similarly selecting Product A would have led to a market share gain of 5%. However, applying the prioritization strategy described above, you could achieve 7.75% in market share gain: Priority Order Product Market Share gain GPUs 1 Product D 1.25% 50 2 Product B 4% 450 3 Product C 2.5% 500 Total 7.75% 1000 Benefits and limitations This alternative prioritization framework introduces a more nuanced and strategic approach. By zeroing in on the Contribution Per GPU, you're strategically aligning resources where they can make the most substantial difference, whether in terms of revenue, market share, or any other defining metric. But the advantages don't stop there. This method also fosters a greater sense of clarity and objectivity across product teams. In my experience, including my early days leading digital transformation at a healthcare company and later while working with various McKinsey clients, this approach has been a game-changer in scenarios where capacity constraints are a critical factor. It's enabled us to prioritize initiatives in a more data-driven and rational way, sidelining the traditional politics where decisions might otherwise fall to the loudest voice in the room. However, no one-size-fits-all solution exists, and it's worth acknowledging the potential limitations of this method. For instance, this approach may not always encapsulate the strategic importance of certain investments. Thus, while exceptions to the framework can and should be made, they ought to be carefully considered rather than the norm. This maintains the integrity of the process and ensures that any deviations are made with a broader strategic context in mind. Conclusion Product leaders are facing an unprecedented situation with the GPU shortage, so finding new ways of managing resources is needed. In the words of the great strategist Sun Tzu, "In the midst of chaos, there is also opportunity." The GPU shortage is indeed a challenge, but with the right approach, it may also be a catalyst for differentiation and success. The proposed prioritization framework, focusing on Contribution Per GPU, offers a strategic way to prioritize. By zeroing in on Contribution Per GPU, companies can maximize their return on investment, aligning resources where they'll make the most impact and focusing on what matters the most to the long-term success of their company. References https://nypost.com/2023/06/07/openais-sam-altman-complained-chip-shortage-is-delaying-chatgpt-plans/

How to Prioritize AI Projects Amidst GPU Constraints

Stepping up the ladder from a can feel like entering a whole new universe. mid-level developer to a senior role Your responsibilities multiply, and suddenly, you're not just juggling coding tasks but also guiding a team, driving project strategy, and constantly learning new skills. And as much as we all wish for more hours in a day, the reality is we only have 24. So how do you manage your time effectively and prioritize all these responsibilities? Why Time Management and Prioritization Matter Time management and prioritization are critical skills that can make or break your productivity, work-life balance, and overall career progression. Effective time management helps you stay focused, reduces stress, and leads to higher work satisfaction. Prioritizing tasks ensures you're making progress on the things that matter most, both in the short-term and long-term. Good thing we're not the first to realize this! We're standing on the shoulders of giants here. Let's learn from those who've come before and take a peek at a couple of strategies they've articulated for us. Implementing the Eisenhower Matrix Ever heard of President Dwight D. Eisenhower? He had quite a knack for time management. Let's borrow a page from his book. "What is important is seldom urgent and what is urgent is seldom important." - Dwight D. Eisenhower This principle forms the backbone of the Eisenhower Matrix, a simple yet profound tool for organizing your tasks. Using the Pareto Principle Next up, meet Vilfredo Pareto, an Italian economist who noticed something intriguing about peas and wealth distribution. Strange combo, I know, but stick with me. This concept, now known as the Pareto Principle or the 80/20 rule, can be a game-changer in prioritizing tasks. Relentlessly focus on the 20% of tasks that yield 80% of the results. Incorporating Time Boxing Let's talk about boxing, but not the kind you're thinking. Time boxing is a time management technique where you allocate a fixed time period, called a time box, to each planned activity. It helps keep you focused and efficient. Define your tasks and allot specific time slots to each. Plan: Work on the task within the set time frame. Act: Evaluate your performance once the time box ends. Review: Prevention is Better Than Cure: Keeping Things Off Your List As developers move up the ranks, the to-do list never seems to get shorter. But what if we could prevent some tasks from making it onto our list in the first place? It's not about shirking responsibilities, but rather about effective delegation and automation. If there are tasks that your juniors can handle, pass them on. Not only will this lighten your load, but it will also provide them with a valuable learning experience. Delegation: Automate routine tasks. As a developer, you have an array of tools at your disposal that can help reduce manual effort and save precious time. (Just don't get nipped by spending hours automating a 5-minute task that happens infrequently.) Automation: Overcoming Procrastination: Starting on Stuck Tasks No matter how well you plan, there always seems to be that one task you've been avoiding. The one that’s been lurking at the bottom of your list, is forever stuck in the 'To Do' column. Here's how to handle such tasks: Large tasks can seem daunting. Try breaking them down into smaller, manageable parts. Then tackle each part one at a time. Break it Down: Quite often, starting is the hardest part. Once you've begun, momentum builds and the task becomes less intimidating. Just Start: Time management and prioritization are critical skills for anyone moving into a senior role. Mastering these skills can help you become more productive, reduce stress, and free up time for continuous learning and personal growth. Remember, it's not about being busy; it's about being productive. – Peter Drucker "Efficiency is doing things right; effectiveness is doing the right things." Start evaluating your tasks, plan your time wisely, and keep your focus on what matters most. In the long run, how you manage your time and prioritize tasks will define your success as a senior developer. Also published here.

Hacking Time Management and Task Prioritization

The Problem of Philosophy, by Bertrand Russells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . HOW A PRIORI KNOWLEDGE IS POSSIBLE HOW A PRIORI KNOWLEDGE IS POSSIBLE Immanuel Kant is generally regarded as the greatest of the modern philosophers. Though he lived through the Seven Years War and the French Revolution, he never interrupted his teaching of philosophy at Königsberg in East Prussia. His most distinctive contribution was the invention of what he called the 'critical' philosophy, which, assuming as a datum that there is knowledge of various kinds, inquired how such knowledge comes to be possible, and deduced, from the answer to this inquiry, many metaphysical results as to the nature of the world. Whether these results were valid may well be doubted. But Kant undoubtedly deserves credit for two things: first, for having perceived that we have a priori knowledge which is not purely 'analytic', i.e. such that the opposite would be self-contradictory, and secondly, for having made evident the philosophical importance of the theory of knowledge. Before the time of Kant, it was generally held that whatever knowledge was a priori must be 'analytic'. What this word means will be best illustrated by examples. If I say, 'A bald man is a man', 'A plane figure is a figure', 'A bad poet is a poet', I make a purely analytic judgement: the subject spoken about is given as having at least two properties, of which one is singled out to be asserted of it. Such propositions as the above are trivial, and would never be enunciated in real life except by an orator preparing the way for a piece of sophistry. They are called 'analytic' because the predicate is obtained by merely analysing the subject. Before the time of Kant it was thought that all judgements of which we could be certain a priori were of this kind: that in all of them there was a predicate which was only part of the subject of which it was asserted. If this were so, we should be involved in a definite contradiction if we attempted to deny anything that could be known a priori. 'A bald man is not bald' would assert and deny baldness of the same man, and would therefore contradict itself. Thus according to the philosophers before Kant, the law of contradiction, which asserts that nothing can at the same time have and not have a certain property, sufficed to establish the truth of all a priori knowledge. Hume (1711-76), who preceded Kant, accepting the usual view as to what makes knowledge a priori, discovered that, in many cases which had previously been supposed analytic, and notably in the case of cause and effect, the connexion was really synthetic. Before Hume, rationalists at least had supposed that the effect could be logically deduced from the cause, if only we had sufficient knowledge. Hume argued—correctly, as would now be generally admitted—that this could not be done. Hence he inferred the far more doubtful proposition that nothing could be known a priori about the connexion of cause and effect. Kant, who had been educated in the rationalist tradition, was much perturbed by Hume's scepticism, and endeavoured to find an answer to it. He perceived that not only the connexion of cause and effect, but all the propositions of arithmetic and geometry, are 'synthetic', i.e. not analytic: in all these propositions, no analysis of the subject will reveal the predicate. His stock instance was the proposition 7 + 5 = 12. He pointed out, quite truly, that 7 and 5 have to be put together to give 12: the idea of 12 is not contained in them, nor even in the idea of adding them together. Thus he was led to the conclusion that all pure mathematics, though a priori, is synthetic; and this conclusion raised a new problem of which he endeavoured to find the solution. The question which Kant put at the beginning of his philosophy, namely 'How is pure mathematics possible?' is an interesting and difficult one, to which every philosophy which is not purely sceptical must find some answer. The answer of the pure empiricists, that our mathematical knowledge is derived by induction from particular instances, we have already seen to be inadequate, for two reasons: first, that the validity of the inductive principle itself cannot be proved by induction; secondly, that the general propositions of mathematics, such as 'two and two always make four', can obviously be known with certainty by consideration of a single instance, and gain nothing by enumeration of other cases in which they have been found to be true. Thus our knowledge of the general propositions of mathematics (and the same applies to logic) must be accounted for otherwise than our (merely probable) knowledge of empirical generalizations such as 'all men are mortal'. The problem arises through the fact that such knowledge is general, whereas all experience is particular. It seems strange that we should apparently be able to know some truths in advance about particular things of which we have as yet no experience; but it cannot easily be doubted that logic and arithmetic will apply to such things. We do not know who will be the inhabitants of London a hundred years hence; but we know that any two of them and any other two of them will make four of them. This apparent power of anticipating facts about things of which we have no experience is certainly surprising. Kant's solution of the problem, though not valid in my opinion, is interesting. It is, however, very difficult, and is differently understood by different philosophers. We can, therefore, only give the merest outline of it, and even that will be thought misleading by many exponents of Kant's system. What Kant maintained was that in all our experience there are two elements to be distinguished, the one due to the object (i.e. to what we have called the 'physical object'), the other due to our own nature. We saw, in discussing matter and sense-data, that the physical object is different from the associated sense-data, and that the sense-data are to be regarded as resulting from an interaction between the physical object and ourselves. So far, we are in agreement with Kant. But what is distinctive of Kant is the way in which he apportions the shares of ourselves and the physical object respectively. He considers that the crude material given in sensation—the colour, hardness, etc.—is due to the object, and that what we supply is the arrangement in space and time, and all the relations between sense-data which result from comparison or from considering one as the cause of the other or in any other way. His chief reason in favour of this view is that we seem to have a priori knowledge as to space and time and causality and comparison, but not as to the actual crude material of sensation. We can be sure, he says, that anything we shall ever experience must show the characteristics affirmed of it in our a priori knowledge, because these characteristics are due to our own nature, and therefore nothing can ever come into our experience without acquiring these characteristics. The physical object, which he calls the 'thing in itself',(1) he regards as essentially unknowable; what can be known is the object as we have it in experience, which he calls the 'phenomenon'. The phenomenon, being a joint product of us and the thing in itself, is sure to have those characteristics which are due to us, and is therefore sure to conform to our a priori knowledge. Hence this knowledge, though true of all actual and possible experience, must not be supposed to apply outside experience. Thus in spite of the existence of a priori knowledge, we cannot know anything about the thing in itself or about what is not an actual or possible object of experience. In this way he tries to reconcile and harmonize the contentions of the rationalists with the arguments of the empiricists. (1) Kant's 'thing in itself' is identical in definition with the physical object, namely, it is the cause of sensations. In the properties deduced from the definition it is not identical, since Kant held (in spite of some inconsistency as regards cause) that we can know that none of the categories are applicable to the 'thing in itself'. Apart from minor grounds on which Kant's philosophy may be criticized, there is one main objection which seems fatal to any attempt to deal with the problem of a priori knowledge by his method. The thing to be accounted for is our certainty that the facts must always conform to logic and arithmetic. To say that logic and arithmetic are contributed by us does not account for this. Our nature is as much a fact of the existing world as anything, and there can be no certainty that it will remain constant. It might happen, if Kant is right, that to-morrow our nature would so change as to make two and two become five. This possibility seems never to have occurred to him, yet it is one which utterly destroys the certainty and universality which he is anxious to vindicate for arithmetical propositions. It is true that this possibility, formally, is inconsistent with the Kantian view that time itself is a form imposed by the subject upon phenomena, so that our real Self is not in time and has no to-morrow. But he will still have to suppose that the time-order of phenomena is determined by characteristics of what is behind phenomena, and this suffices for the substance of our argument. Reflection, moreover, seems to make it clear that, if there is any truth in our arithmetical beliefs, they must apply to things equally whether we think of them or not. Two physical objects and two other physical objects must make four physical objects, even if physical objects cannot be experienced. To assert this is certainly within the scope of what we mean when we state that two and two are four. Its truth is just as indubitable as the truth of the assertion that two phenomena and two other phenomena make four phenomena. Thus Kant's solution unduly limits the scope of a priori propositions, in addition to failing in the attempt at explaining their certainty. Apart from the special doctrines advocated by Kant, it is very common among philosophers to regard what is a priori as in some sense mental, as concerned rather with the way we must think than with any fact of the outer world. We noted in the preceding chapter the three principles commonly called 'laws of thought'. The view which led to their being so named is a natural one, but there are strong reasons for thinking that it is erroneous. Let us take as an illustration the law of contradiction. This is commonly stated in the form 'Nothing can both be and not be', which is intended to express the fact that nothing can at once have and not have a given quality. Thus, for example, if a tree is a beech it cannot also be not a beech; if my table is rectangular it cannot also be not rectangular, and so on. Now what makes it natural to call this principle a law of thought is that it is by thought rather than by outward observation that we persuade ourselves of its necessary truth. When we have seen that a tree is a beech, we do not need to look again in order to ascertain whether it is also not a beech; thought alone makes us know that this is impossible. But the conclusion that the law of contradiction is a law of thought is nevertheless erroneous. What we believe, when we believe the law of contradiction, is not that the mind is so made that it must believe the law of contradiction. This belief is a subsequent result of psychological reflection, which presupposes the belief in the law of contradiction. The belief in the law of contradiction is a belief about things, not only about thoughts. It is not, e.g., the belief that if we think a certain tree is a beech, we cannot at the same time think that it is not a beech; it is the belief that if the tree is a beech, it cannot at the same time be not a beech. Thus the law of contradiction is about things, and not merely about thoughts; and although belief in the law of contradiction is a thought, the law of contradiction itself is not a thought, but a fact concerning the things in the world. If this, which we believe when we believe the law of contradiction, were not true of the things in the world, the fact that we were compelled to think it true would not save the law of contradiction from being false; and this shows that the law is not a law of thought. A similar argument applies to any other a priori judgement. When we judge that two and two are four, we are not making a judgement about our thoughts, but about all actual or possible couples. The fact that our minds are so constituted as to believe that two and two are four, though it is true, is emphatically not what we assert when we assert that two and two are four. And no fact about the constitution of our minds could make it true that two and two are four. Thus our a priori knowledge, if it is not erroneous, is not merely knowledge about the constitution of our minds, but is applicable to whatever the world may contain, both what is mental and what is non-mental. The fact seems to be that all our a priori knowledge is concerned with entities which do not, properly speaking, exist, either in the mental or in the physical world. These entities are such as can be named by parts of speech which are not substantives; they are such entities as qualities and relations. Suppose, for instance, that I am in my room. I exist, and my room exists; but does 'in' exist? Yet obviously the word 'in' has a meaning; it denotes a relation which holds between me and my room. This relation is something, although we cannot say that it exists in the same sense in which I and my room exist. The relation 'in' is something which we can think about and understand, for, if we could not understand it, we could not understand the sentence 'I am in my room'. Many philosophers, following Kant, have maintained that relations are the work of the mind, that things in themselves have no relations, but that the mind brings them together in one act of thought and thus produces the relations which it judges them to have. This view, however, seems open to objections similar to those which we urged before against Kant. It seems plain that it is not thought which produces the truth of the proposition 'I am in my room'. It may be true that an earwig is in my room, even if neither I nor the earwig nor any one else is aware of this truth; for this truth concerns only the earwig and the room, and does not depend upon anything else. Thus relations, as we shall see more fully in the next chapter, must be placed in a world which is neither mental nor physical. This world is of great importance to philosophy, and in particular to the problems of a priori knowledge. In the next chapter we shall proceed to develop its nature and its bearing upon the questions with which we have been dealing. 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. Bertrand Russell (2009). THE PROBLEMS OF PHILOSOPHY. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/5827/5827-h/5827-h.htm 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 .

How a Priori Knowledge is Possible

Over the years in product management, I've come to recognize the true significance of one skill — prioritization. It's an art that product managers must master to ensure a product's success: guiding the product development cycle, and determining what features make it to the users, and when. Considering diverse customer needs, fierce competition, and limited resources, effective prioritization is the key to unlocking true value. In my search for effective prioritization methods, several frameworks have proven to be useful. Each of them offers unique perspectives and approaches to prioritizing features and tasks. These frameworks, however, shine in different scenarios, and understanding when to use which framework is just as important as understanding the frameworks themselves. Let's figure this out together. The RICE Scoring Model The name of this model is from an acronym for Reach, Impact, Confidence, and Effort. Therefore, the core of the RICE scoring model consists of four components: This factor refers to the number of users or customers impacted by a task or feature within a given period. Reach is an important determinant of a task's value as it quantifies the breadth of its potential impact. Reach: : it assesses the degree of change a task or feature will have on an individual user's experience or the business. The larger the positive change a task can bring, the higher its impact score. Impact this is a measure of certainty about your estimates. It's natural to have varying degrees of confidence about different tasks, especially when dealing with assumptions or forecasts. The higher your confidence in the reach, impact, and effort estimates, the higher the confidence score. Confidence: Effort estimates the total amount of work a task will require from all team members to complete. This typically includes design, development, testing, and other associated costs. Tasks that require less effort get a higher score. Effort: The RICE scoring model is particularly useful in situations that demand a data-driven, balanced approach to decision-making. It considers both the potential benefits of a task (Reach and Impact) and the resources required to execute it (Effort), while keeping the certainty of estimates in check (Confidence). This view allows product managers to avoid the common pitfall of prioritizing tasks based on intuition or bias. Using the RICE scoring model effectively involves regularly updating the scores as new information emerges and reassessing the priorities accordingly. It encourages transparency and open discussion, as all team members can understand the reasoning behind prioritization decisions. The ICE Scoring Model This is a simplified version of the RICE framework for making easier decisions. Here, ICE is an acronym for Impact, Confidence, and Ease. Again, this method allows product managers to assess and rank tasks by considering their overall impact and ease of implementation. Here's what each component of the ICE Scoring Model represents: This is a slightly changed variation of the previous model. Again, we’re assessing several factors. this refers to the potential effect of a task or feature on your product, business, or users. Impact: like before, this is all about how sure you are about your impact and ease estimates. Confidence: this measures how easy or difficult it is to implement the task. Effort: Using the ICE Scoring Model is fairly straightforward. You give each task a score from 1 to 10 for each component — impact, confidence, and ease. Then, you calculate the ICE score by finding the average of these three scores. Tasks are then ranked based on their ICE score, and then you put those with higher score first in you to-do list. The ICE Scoring Model comes into its own when you're grappling with tasks where the reach isn't a primary concern or is hard to estimate. It helps you zero in on the tasks that can make the biggest impact and are relatively easy to implement, enabling a focused approach towards resource allocation. The MoSCoW Method This method comes from the world of software development and project management, but it has since found its home in the toolkits of product managers. Yet again, it is an acronym that represents four categories of tasks: Must have, Should have, Could have, and Won't have. these are the non-negotiables, the essential elements without which the product simply cannot function or launch. Must have: tasks falling under this category are important but not critical for the initial launch or operation. If neglected, “Should have” tasks might cause inconvenience or impair some functionalities, but the product remains usable. Should have: these tasks are nice-to-have features or elements. They enhance the user experience or add value but aren't fundamental to the product's basic operation. They are often first candidates for scope cuts if time or resources become tight. Could have: the final category is often overlooked, yet crucial. It outlines the tasks or features that will not be implemented in the current phase of the project. Identifying “Won't have” items provides clarity, sets expectations right, and ensures you can focus on more important tasks. Won't have: The MoSCoW method is beneficial in situations where decisions are driven by expertise and intuition, in scenarios with limited data but a strong internal perception of significance. The Kano Model Understanding and predicting customer satisfaction is beyond important in product management. Here, the Kano Model can help you. Named after its creator, Noriaki Kano, it provides a structured way to categorize features based on their potential impact on customer satisfaction. It includes five classifications: “Must-be”, “One-Dimensional”, “Attractive”, “Indifferent”, and “Reverse”. these are basic expectations that customers have, even if they don't expressly state them. They are so fundamental to the product that their absence would cause great dissatisfaction. However, just having these features doesn't lead to increased satisfaction since customers view these as a given. Must-be Features: these are the features that linearly impact customer satisfaction. The better these features perform, the higher the satisfaction, and vice versa. Customers often explicitly express their needs for such features. One-Dimensional Features: these are the lovely extras, the features customers didn't know they wanted until they saw them. They can significantly enhance customer satisfaction, but their absence doesn't cause frustration since customers aren't expecting them. Attractive Features: these are features towards which customers are neutral. Their presence or absence doesn't significantly impact customer satisfaction. Identifying such features is crucial to avoid investing resources in areas that won't bring you substantial returns. Indifferent Features: these are features that can lead to dissatisfaction when present. Although, different customer segments may react oppositely to the same feature, making it attractive for some and a reverse feature for others. Reverse Features: Applying the Kano Model involves continuous customer feedback and market research. As customer preferences and market trends evolve, so too will the categorization of features within the model. The Weighted Scoring Model in Product Management In product management, decision-making often requires balancing multiple criteria at once. The Weighted Scoring Model can help you navigate this process. It gives you a systematic approach to evaluating tasks against various criteria, each weighted according to its relative importance. The model operates on a simple principle: not all criteria are created equal. Each criterion is assigned a weight reflecting its importance in the decision-making process. Tasks are then scored against each one of them, and an overall score is calculated by summing the weighted scores. Then, the tasks are ranked based on these overall scores, with higher scores meaning higher priority. Here's how the Weighted Scoring Model works in practice. Start by identifying the criteria relevant to the decision at hand. These could include factors such as potential impact, strategic alignment, cost, risk, or anything else important to your specific context. Assign a weight to each criterion to reflect its relative importance. The weights should add up to 100% (or 1 if you're using a decimal scale). Score each task against each criterion on a scale (for example, 1–10). The scores should reflect how well the task fulfills the criterion. Multiply the score of each task for each criterion by the weight of that criterion to get the weighted score. Add up the weighted scores of each task to get the total score. Rank the tasks based on their total scores. It allows you to make more nuanced decisions by considering all relevant criteria and their respective weights. It lowers the risk of overlooking key factors or giving unnecessary importance to others. The Weighted Scoring Model is particularly useful when you're dealing with multiple factors that vary in importance. This method is particularly effective when there are many stakeholders involved in making a decision. It helps narrow down their opinions to an unbiased formula that determines the priority of the task. The Buy a Feature Method Product management is not a solo activity. It requires the involvement of various stakeholders, from team members to customers to senior executives. The Buy a Feature method makes use of their collective wisdom. It engages stakeholders by giving them “money” to invest in the features they think are the most important. Participants are then given an amount of virtual money and invited to “buy” the features they believe to be most valuable. The features with the biggest funding at the end of the exercise gain priority. The method is quite straightforward: each feature or task is assigned a cost proportional to its complexity or effort needed for its implementation. This approach promotes discussions among stakeholders as they negotiate and collaborate to fund their preferred features. The process can bring you valuable insights beyond just the rank of features. It exposes differing perspectives, unveils hidden assumptions, and surfaces previously unconsidered factors. It's a powerful tool to understand what features matter most to your audience, be it customers, team members, or other interested parties. It can also serve as a platform to educate stakeholders about the complexities and trade-offs of product development, giving them a better understanding and appreciation of the process. The Buy a Feature method is particularly effective in scenarios where stakeholder input is crucial. This method is suitable for startups with a few stakeholders, but as the company grows, it is usually replaced by other, more comprehensive approaches. Story Mapping in Product Management This method involves arranging user stories — descriptions of tasks or features from their perspective — into a meaningful model to visualize the system's functionality, find gaps, and prioritize work. To release a great product, you need to understand the user's journey — something you can achieve using Story Mapping. Story Mapping arranges user stories into a two-dimensional matrix. The horizontal axis follows the person’s journey through the product, while the vertical axis represents the priority or complexity of the tasks. User stories are placed along these axes, creating a visual map that depicts how users interact with the product and which tasks are more critical or complex. Story Mapping can be particularly useful when working with Agile methodologies and user-centric business models. In Agile, the focus is on delivering value incrementally and adapting to changes. The visual and flexible nature of Story Mapping fits this approach perfectly. It helps teams understand what value each increment should deliver and adapt the map as they learn more about the users and the product. The Opportunity Scoring Model The most successful products are those that solve genuine customer problems. But how do you understand which problems are worth addressing? That's when you use the Opportunity Scoring model. Designed to evaluate potential opportunities in the market, it focuses on two crucial factors: the prevalence of a problem and the customer's satisfaction with existing solutions. The Opportunity Scoring model employs a two-dimensional approach: : this dimension assesses how widespread a particular customer problem is. It quantifies the number of potential customers who can experience this issue. Problem Prevalence : this gives you an idea of how satisfied your customers are with the current solutions to their problem. Current Solution Satisfaction By plotting these two dimensions, the Opportunity Scoring model helps identify areas where a large number of customers are dissatisfied with current solutions — a potential sweet spot for product innovation. The Cost of Delay Framework As a rule, every delay can have a significant impact on a product's success, and the Cost of Delay framework helps you here. It takes into account the economic or financial impact of delaying the release of certain features or tasks. It's calculated by considering three main factors: the benefit or value the user gets from the feature or project. Delaying could mean lost user satisfaction, usability, or even market share. User Value: the extent to which the value of the feature diminishes over time. For instance, a feature related to a seasonal event like a holiday promotion could lose most of its value if delayed. Time Criticality: the potential lost opportunities or increased risks due to delay. This could include factors like competitor activity, market shifts, or compliance risks. Risk/Opportunity Cost: By assessing and quantifying these factors, the Cost of Delay framework provides a monetary value representing the cost incurred for each time unit of delay. This value can be used to compare and prioritize features or tasks, with those having a higher Cost of Delay being prioritized. This framework is beneficial when economic or financial factors are crucial to the prioritization process. It highlights the features that should be fast-tracked to avoid significant costs and those where delays would be less impactful. The real goal of prioritization is making well-informed decisions to deliver maximum value to your customers and your company. It's about choosing the most impactful tasks to work on and knowing when to work on them. The frameworks we've talked about are tools to help you make those decisions. Each is useful in its own way, and using them wisely and effectively can greatly improve your product management process. Though, prioritization isn't a one-and-done thing. It's a path that involves many decisions and learning experiences, often with multiple iterations. As you gain experience and knowledge in product management, you'll find these frameworks more and more useful. However, in product management, the journey is often just as rewarding as the destination. Safe travels!

Prioritization Frameworks, and When Do They Work Better

‘Am I talking to a brick wall?’ is a common Irish saying that translates to' are you listening to what I am trying to tell you? This frustration must be so common for activists and sustainability advocates who see the same mistakes, same discussions and same solutions being promoted when assessing the needs of future generations. According to the Credit Suisse Global , in 2021 the world’s richest 1 percent, those with more than $1 million, own 47.8 percent of all the world’s wealth. This is up from 45.8 percent of total wealth share in 2020. Their data also show that adults with less than $10,000 in wealth make up 53.2 percent of the world’s population. The enormous inequalities we continue to see across all counties is not sustainable if future generations are going to thrive and cater for their families. Wealth Report The time to change behavior and encourage the adoption of newly developed solutions is here. As long as industrial leaders have the willingness to try, regenerative finance offers the potential to have vast, positive consequences for large swaths of people who have been locked out of global financial systems to date. Last week I sat down with Gabriela Chang, CSO and Co-Founder of EthicHub, a leading project in the world of regenerative finance. Chang speaks from on the ground experience working with farms in Latin America. Connecting small farm holders with the means to support their families and production through ReFi is an inspirational vision. Not only does it have a long terms positive impact, it has completely overhauled accessibility to impact investment projects from global lenders and direct buyers who want to support the changes needed for a sustainable future. Challenging the status quo The collaborative approach between ReFi projects using blockchain solutions is a broader way in which emerging technologies can be used to tackle some of the biggest problems that we face. In a world plagued by social and economic disparities, a transformative force is emerging. ReFi challenges the status quo of traditional finance and seeks to broaden how we think about using our financial resources. Let’s get acquainted with Regenerative Finance, a burgeoning field with the potential to revolutionize the impact that individuals and organisations have when it comes to creating sustainable environments and improving living standards. What is ReFi? Regenerative Finance, aka ReFi, represents a departure from the profit-driven mindset that has dominated traditional finance for decades. Instead, it focused on circular economies. This form of finance exists to promote sustainability and offer financial rewards for investing in transparent, blockchain based financial services that give back to underserved environments and communities. It has the potential to foster financial inclusion through decentralized ecosystems and provide much needed financial resources to sustainable infrastructure and resources that take care of our environment. Examples include Ethichub, Bitgreen, Regen Network and Geoblocs. Enabling Communities to access much needed funds What sets ReFi apart is its commitment to empowering communities and granting them control over their financial systems. By prioritizing sustainability, ReFi enables communities to have access to more financial resources and take charge of their economic situation. Ethichub for instance is working directly with farming communities to provide new means of financial support. Moreover, ReFi is using innovative technology to provide a secure and transparent way to promote a more resilient food system. In the past we have spoken about the challenges facing the consumer when they want to support sustainable agriculture. With ReFi gaining more traction, it is worth considering the implications for small, working, farming communities. In the future individuals and organisations will be able to track the communities that they are supporting from production to distribution. It is not difficult to disassociate from the farmers who produce the products that line our supermarket shelves. However, more consumers are interested in understanding the short and long term impact of their shopping behavior. As the green-friendly consumer buying trend continues, organisations need to be proactive in their approach to sustainability and ReFi provides an opportunity to create value within these farming communities, giving vital resources to those who need it most. Driving real world change using Web3 technologies ReFi presents an exciting opportunity for builders and founders to drive real change. By leveraging innovative Web3 technologies, the potential for reinventing the financial infrastructure is unprecedented. ReFi has introduced new business models that reward sustainable approaches to farming practices. By combining investment opportunities with the creation of green friendly business practices, a whole new market for sustainability opens up. So, how can individuals get involved in the ReFi movement? The key lies in collaboration and action. Joining organizations like the Climate Collective or applying to accelerators like Celo Camp offers opportunities for mentorship, funding, and community engagement. Founders and builders with ideas to change the world should seize this moment to contribute to the ReFi ecosystem and help shape a brighter future for all.

ReFi: The Financial Revolution That Prioritizes the Planet

Mayur Kamat Unveils Binance's Big Bets in Innovation and DeFi In the dynamic world of cryptocurrencies and blockchain, product managers play a pivotal role in shaping the future. Their decisions steer the development of a company's products and determine its market position. Today, we are delighted to present our conversation with Mayur Kamat, a leading figure in product management at . Having started his career at , he is now a major driving force behind Binance's impressive portfolio of products. In this interview, Mayur shares his unique perspectives on a range of topics, from prioritizing product features to harnessing AI technologies, and from promoting DeFi to the exciting future of the cryptocurrency industry. Binance Microsoft Building the Future of Crypto: A Conversation with Binance Product Manager Mayur Kamat Ishan Pandey: Hi Mayur Kamat , great to have you here for our "Behind the Startup" series. Please tell us about yourself and what inspired you to pursue a career in product management? Mayur Kamat: I started my career as a product manager, before it was even a thing. I joined Microsoft as an intern and that eventually became the product manager role. And I loved it - I got to work with some of the OGs of the industry and they built stuff that I came up with! It was love at first sight. Ishan Pandey: How do you prioritize features and improvements for Binance's products, and what factors do you take into account when making these decisions? Mayur Kamat : Binance has a massive portfolio of products. People know us for our exchange but we have 40-50 other products. Given the breadth of the portfolio, the same approach for prioritization doesn’t work across the board. I think of them in 3 big categories: - this would include products that we develop as part of our commitment to improve the industry - for example . Or it could also be something we need to build for legal and regulatory needs. These products are extremely important and usually have an urgent timeline and high cost if we anything goes wrong. We make very deliberate product decisions here and execute them rapidly. Commitments to the community and the industry Proof of Reserves Looking at how we grow and optimize ‘mature’ products - Binance is less than 6 years old so it is funny to think of our products as mature but they are. - these are massively used products. Here we focus on innovation and experimentation. Try lots of ideas, ship daily, fail fast, learn and iterate. The core Binance app, exchange, trading, etc - these are our creative, maybe some might say ‘crazy’ bets. Here we take big bets, we aim to get to market soon with an MVP and then see what our community thinks about it. The Binance Tax tool is a great example of this - we built it and initially launched it in France earlier this year. Seeing the positive reaction to the initial version from the community, we’ll now look at bringing this to more markets around the world soon. Innovation big bets Ishan Pandey: Can you discuss the development of Binance's Defi Wallet and how do you see the wallet differentiating itself from other wallet solutions in the market, and what unique features or capabilities it offers? Mayur Kamat: The Binance DeFi wallet started with a very simple premise - blockchains are still hard to use for everyday users. We cannot bring a billion users to crypto without wallets that work like modern technology - simple and safe. Creating a wallet, connecting to , understanding the multi-chain world, transferring funds to addresses - all of it is complicated and stressful. Even the OGs mess up. How can we make wallets working like a basic web2 app where you login and things just work? That’s what we are working towards. dApps On differentiation, there are some obvious advantages when you use the Binance DeFi wallet and are an existing Binance user. You don't need to login again, you don’t need back-up keys or to remember addresses - it is all very seamless. We will add a lot more features soon for users who use both + DEX, and we believe everyone will soon will. We also believe that innovation like this pushes other wallets to get easier to use too - the whole industry wins when things get simpler. CEX Ishan Pandey: Can you talk about how Binance's Defi Wallet SDK enables developers to build dApps that integrate with Binance's platform, and how it has been received by the developer community? Mayur Kamat: It is early days. Our first version of the SDK is for dApps and we are seeing big interest, especially from the BNB chain developer community. The SDK will soon allow for new use cases like portfolio mgmt, wallet as a service, etc. We believe we can abstract a lot of the boring (but critical) layers from Web3 developers, so they can focus on building the magic. Ishan Pandey: What role do you see Binance's Defi Wallet and SDK playing in advancing the development of the decentralized finance (DeFi) ecosystem, and how do you plan to position Binance in this space? Mayur Kamat: It’s an important foundational piece that we need for the ecosystem to grow. If 1 billion users had a crypto wallet, the DeFi world would be several orders of magnitude bigger. It’s that simple. Internet mass adoption did not happen till Web1 made IP addresses, ports, DNS, etc abstracted. We want to do the same for Web3. Ishan Pandey: How do you ensure the security and reliability of Binance's Defi Wallet and SDK, and what measures does the company have in place to protect user funds and data? Mayur Kamat: The Web3 wallet is different from Binance Exchange. The funds are held by the user on their own addresses. Binance just helps with the key management, which are stored securely using our core infrastructure that protects Binance CEX assets. The Binance DeFi wallet uses MPC which means that MPC key shares are stored on different infrastructures to avoid a single point of failure, making it safe and secure. And use our biometrics based FIDO authentication to both improve security and ease of use. Ishan Pandey: What impact do you believe AI-powered technologies will have on Binance's products and users, and how do you plan to integrate these technologies into your product development strategies? Mayur Kamat: What we are seeing with the recent pace of developments with generative AI, every company in the world will need to include it in their roadmap, or else run the risk of being outclassed by their existing and upcoming competitors. Binance is no exception and we are looking at it across the board in product. We have been a big user of ML and AI in Binance for years - in fraud detection, compliance, security monitoring, personalization, etc. This year, we took this a step further a  launched 2 new generative AI products - Bicasso NFT generator and Binance Sensei chatbot for crypto education with Binance Academy. The industry is moving quickly - we will keep trying out things in this space and will see what resonates with our users. Ishan Pandey: Can you comment on the current state of the cryptocurrency industry and the direction you expect it to take over the next few years? Mayur Kamat: I think of the crypto industry based on user and business adoption, not on coin prices. And based on that metric, we are seeing a strong foundation. As long as we solve real user problems, we will grow the industry. Remittances, cross-border settlement, inflation-hedge, financial access to under-privileged, 24/7 global trading, automated settlement, automating contracts, simplified and low cost payments - all of these are trillion dollar problems and blockchain is solving them today. But in small %, single digit. As long as we keep increasing that number, the industry will grow. Ishan Pandey: In your opinion, what are some of the most promising trends in the cryptocurrency industry, and how is Binance positioning itself to capitalize on these trends? Mayur Kamat: The industry and the trends within it change so rapidly, so we don’t “forward plan” as such. We listen to our community everyday and build products they want. We get thousands of pieces of feedback every month about what the community wants. As long as we do right by the users, Binance will grow because the industry will continue to grow. Trends come and go - we try to focus on the core of our tenets - user focused, community-driven, transparent and agile. We also ask our community to tell us direct if they have ideas for product features, so if your readers are reading this and have a great idea they want to share, please let us know here: https://www.binance.com/en/my/user-support/feedback/entry

Decoding Binance's Product Prioritization Strategy with Mayur Kamat (Head of Product)

Decodificación de la estrategia de priorización de productos de Binance con Mayur Kamat (jefe de producto)

Décryptage de la stratégie de priorisation des produits de Binance avec Mayur Kamat (Head of Product)

मयूर कामत (उत्पाद के प्रमुख) के साथ बिनेंस की उत्पाद प्राथमिकता रणनीति को डिकोड करना

マユール・カマト氏（製品責任者）によるバイナンスの製品優先順位付け戦略の解読

Decodificando a estratégia de priorização de produtos da Binance com Mayur Kamat (chefe de produto)

Giải mã chiến lược ưu tiên sản phẩm của Binance với Mayur Kamat (Trưởng bộ phận sản phẩm)

与 Mayur Kamat（产品负责人）解读币安的产品优先排序策略

You're waiting at the front-desk of 's campus in Boulder, Colorado, waiting for your coding interview to start. Across from you in the lobby sit two other candidates: Google A charismatic airpods-flaunting developer who can charm the pants off of anyone in the room but requires an agonizing 8 hours to get a linked list working A programming prodigy who writes games in assembly, but is such a colossal jerk that nobody will play their game anyways Which of them will provide more fierce competition for the job spot? Let's tackle the age-old question: Which is more important, hard or soft skills for programmers? First I'll annoyingly explain what hard and soft skills are, but then we'll dive into the reason I wanted to write this article: . This analogy will help you understand where you should be spending your time improving to maximize your chances of landing a job. the rectangle analogy Hard Skills: The Foundation First things first: what are hard skills? These are the tangible, measurable abilities that you to get the job done. As a developer, you'll need to: need - like , JavaScript, or Golang. Code in specific languages Python - such as Git, Docker, or AWS. Use technologies and tools - because nobody wants to be that developer who can't reverse a string without Googling it first. Understand and data structures algorithms - because programming is a vast and ever-expanding field. A boatload of other things Importance of hard skills for landing a job Hard skills are the bread and butter of your programming career. They're an absolute requirement for most programming positions, and they demonstrate your technical competency. In fact, if you could only have one type of skill, hard skills would be more important. After all, it's pretty tough to write code without, you know, actually knowing how to code. Keep hardening your hard skills The tech world moves at breakneck speed, and if you're not continuously leveling up your hard skills, you're gonna get left in the dust. Remember, Rome wasn't built in a day, and neither is your programming expertise. Invest time and effort in learning new languages, tools, and techniques over time. You don't need to be an expert on , but you need to know how to deliver working code that solves real problems within your domain. If you want to be a and work at SaaS companies, then you should at the very least: everything back-end developer Have a solid understanding of a couple of programming languages like Python and Go Be able to set up and use a SQL database Know how to build a using those technologies REST API Know how to deploy that API to a cloud platform like AWS or GCP Soft Skills Soft skills, on the other hand, are the intangible, interpersonal abilities that can give your career nice boots. They might not be as easy to quantify as hard skills but they're nearly as vital. Soft skills include: - people like working with fun people. Charisma - so you can articulate your ideas and keep everyone in the loop. Communication and writing abilities - because you're working with humans, and humans don't like assholes. Kindness and empathy - if you're good, but you don't deliver, then no one cares. Hard work and follow-through Importance of soft skills for long-term success Soft skills are the secret sauce that can propel your career to new heights. They facilitate collaboration and teamwork, making it easier for you to work with others and get the job done. Plus, having solid soft skills can enhance your overall job satisfaction and career growth. After all, nobody wants to work with a genius who's also a jerk. Hard skills are a requirement to get a job in tech, but assuming the gap in hard skills between two candidates is similar, I've actually seen the more charismatic and likable candidate get the job over the slightly more technically competent one. Why? Managers are people, and people like working with other people they like. How do I improve my soft skills? If you have a crappy personality, don't worry too much. You don't need to be a brilliant orator or a master of the written word just to get a job as a code monkey. The number one rule should be easy to follow: If you do that, you're halfway there. don't be an asshole. The related follow-up rule is to balance . If you're a kind, open-minded developer that knows what you're talking about, you'll do great. confidence and humility The Rectangle Analogy: Combining Hard and Soft Skills Now that we've explored both hard and soft skills, let's dive into the rectangle analogy to understand how they work together. Imagine your hard skills are the length of a rectangle, while your soft skills represent the width. The area of this rectangle corresponds to your probability of landing a job. The larger the area, the better your chances of finding a gig that suits your skillset. This means the equation for your job probability is: hard_skills * soft_skills = job_probability To maximize with the least amount of effort, spend more time on whichever skill you tend to lack. The area of a square is larger than the area of a lopsided rectangle with the same perimeter. job_probability The misconception of limited skill points If you're thinking this sounds a bit like a Dungeons & Dragons character sheet where you have a limited number of skill points to allocate, that's not how the real world works. In the real world, you can just . You can excel in both hard and soft skills. get more skill points I've worked with amazing people who are easy to get along with and are brilliant programmers I've also worked with incompetent jerks who are somehow also bad at writing code If you work hard and do your best, you can be both a great programmer and a great person. It's not an either-or situation. In the immortal words of Shia LaBeouf, "Just do it!" https://www.youtube.com/watch?v=ZXsQAXx_ao0&embedable=true

Should Programmers Prioritize Hard Skills or Soft Skills?

প্রোগ্রামারদের কি হার্ড স্কিল বা সফট স্কিলকে অগ্রাধিকার দেওয়া উচিত?

Sollten Programmierer Hard Skills oder Soft Skills priorisieren?

¿Deberían los programadores priorizar las habilidades duras o las blandas?

Les programmeurs devraient-ils privilégier les compétences techniques ou les compétences générales ?

क्या प्रोग्रामर्स को हार्ड स्किल्स या सॉफ्ट स्किल्स को प्राथमिकता देनी चाहिए?

プログラマーはハードスキルとソフトスキルのどちらを優先すべきか?

프로그래머는 하드 스킬과 소프트 스킬 중 어느 것을 우선시해야 할까요?

Os programadores devem priorizar Hard Skills ou Soft Skills?

Programcılar Sert Becerilere mi Yoksa Sosyal Becerilere mi Öncelik Vermeli?

Lập trình viên nên ưu tiên kỹ năng cứng hay kỹ năng mềm?

程序员应该优先考虑硬技能还是软技能？

Kubernetes has been widely adopted, and many organizations use it as their de-facto orchestration engine for running workloads that need to be created and deleted frequently. Therefore, proper scheduling of the pods is key to ensuring that application pods are up and running within the Kubernetes cluster without any issues. This article delves into the use cases around resource management by leveraging the object to protect mission-critical or high-priority pods from getting evicted and making sure that the application pods are up, running, and serving traffic. PriorityClass Resource Management in Kubernetes The control plane consists of multiple components, out of which the scheduler (usually the built-in ) is one of the components which is responsible for assigning a node to a pod. kube-scheduler Whenever a pod is created, it enters a "pending" state, after which the scheduler determines which node is best suited for the placement of the new pod. In the background, the scheduler runs as an infinite loop looking for pods without a set that are . For each Pod that needs scheduling, the scheduler tries to decide which node should run that Pod. nodeName ready for scheduling If the scheduler cannot find any node, the pod remains in the pending state, which is not ideal. , , , the rank of nodes based on available resources (for example, CPU and memory), and several other criteria are used to determine the pod's placement. Note: To name a few, nodeSelector taints and tolerations nodeAffinity The below diagram, from point number 1 through 4, explains the request flow: Typical use cases Below are some real-life scenarios where control over the scheduling and eviction of pods may be required. Let's say the pod you plan to deploy is critical, and you have some resource constraints. An example would be the DaemonSet of an . The Loki pods must run before other pods can on every node. In such cases, you could ensure resource availability by manually identifying and deleting the pods that are not required or by adding a new node to the cluster. Both these approaches are unsuitable since the former would be tedious to execute, and the latter could involve an expenditure of time and money. infrastructure component like Grafana Loki Another use case could be a single cluster that holds the pods for the below environments with associated priorities:
- Production ( ):  top priority
- Preproduction ( ): intermediate priority
- Development ( ): least priority <br/><br/>In the event of high resource consumption in the cluster, there is competition for CPU and memory resources on the nodes. While cluster-level autoscaling add more nodes, it takes time. In the interim, if there are no further nodes to scale the cluster, some Pods could remain in a Pending state, or the service could be degraded as they compete for resources. If the kubelet does evict a Pod from the node, that eviction would be random because the kubelet doesn’t have any special information about which Pods to evict and which to keep. prod preprod dev may A third example could be a microservice backed by a queuing application or a database running into a resource crunch and the queue or database getting evicted. In such a case, all the other services would be rendered useless until the database can serve traffic again. There can also be other scenarios where you want to control the order of scheduling or order of eviction of pods. PriorityClasses in Kubernetes PriorityClass is a cluster-wide API object in Kubernetes and part of the API group. It contains a mapping of the PriorityClass name (defined in ) and an integer value (defined in ). This represents the value that the scheduler uses to determine Pod's relative priority. scheduling.k8s.io/v1 .metadata.name .value Additionally, when you create a cluster using kubeadm or a (for example, Azure Kubernetes Service), Kubernetes uses PriorityClasses to safeguard the pods that are hosted on the control plane nodes. This ensures that critical cluster components such as CoreDNS and kube-proxy can run even if resources are constrained. managed Kubernetes service This availability of pods is achieved through the use of a special PriorityClass that ensures the pods are up and running and that the overall cluster is not affected. $ kubectl get priorityclass
NAME                      VALUE        GLOBAL-DEFAULT   AGE
system-cluster-critical   2000000000   false            82m
system-node-critical      2000001000   false            82m The diagram below shows exactly how it works with the help of an example, which will be detailed in the upcoming section. Pod priority and preemption is a Kubernetes feature that allows the cluster to preempt pods (removing an existing Pod in favor of a new Pod) on the basis of priority. indicates the importance of a pod relative to other pods while scheduling. If there aren't enough resources to run all the current pods, the scheduler tries to evict lower-priority pods over high-priority ones. Pod preemption Pod priority Also, when a healthy cluster experiences a node failure, typically, lower-priority pods get preempted to create room for higher-priority pods on the available node. This happens even if the cluster can bring up a new node automatically since pod creation is usually much faster than bringing up a new node. PriorityClass requirements Before you set up PriorityClasses, there are a few things to consider. Decide which PriorityClasses are needed. For instance, based on environment, type of pods, type of applications, etc. The default PriorityClass resource for your cluster. The pods without a will be treated as priority 0. priorityClassName Use a consistent naming convention for all PriorityClasses. Make sure that the pods for your workloads are running with the right PriorityClass. PriorityClass hands-on example Let’s say there are 3 application pods: one for prod, one for preprod, and one for development. Below are three sample YAML manifest files for each of those. ---
# development
apiVersion: v1
kind: Pod
metadata:
  name: dev-nginx
  labels:
    env: dev
spec:
  containers:
  - name: dev-nginx
    image: nginx
    resources:
      requests:
        memory: "256Mi"
        cpu: "0.2"
      limits:
        memory: ".5Gi"
        cpu: "0.5" ---
# preproduction
apiVersion: v1
kind: Pod
metadata:
  name: preprod-nginx
  labels:
    env: preprod
spec:
  containers:
  - name: preprod-nginx
    image: nginx
    resources:
      requests:
        memory: "1.5Gi"
        cpu: "1.5"
      limits:
        memory: "2Gi"
        cpu: "2" ---
# production
apiVersion: v1
kind: Pod
metadata:
  name: prod-nginx
  labels:
    env: prod
spec:
  containers:
  - name: prod-nginx
    image: nginx
    resources:
      requests:
        memory: "2Gi"
        cpu: "2"
      limits:
        memory: "2Gi"
        cpu: "2" You can create these pods with the command, and then check their status using the command. You can see if they are up and look ready to serve traffic: kubectl create -f <FILE.yaml> kubectl get pods $ kubectl get pods --show-labels
NAME            READY   STATUS    RESTARTS   AGE   LABELS
dev-nginx       1/1     Running   0          55s   env=dev
preprod-nginx   1/1     Running   0          55s   env=preprod
prod-nginx      0/1     Pending   0          55s   env=prod Bad news. The pod for the Production environment is still Pending and isn't serving any traffic. Let's see why this is happening: $ kubectl get events
...
...
5s          Warning   FailedScheduling   pod/prod-nginx      0/2 nodes are available: 1 Insufficient cpu, 2 Insufficient memory. In this example, there is only one worker node, and that node has a resource crunch. Now, let's look at how PriorityClass can help in this situation since prod should be given higher priority than the other environments. PriorityClass API Before creating PriorityClasses based on these requirements, let's see what a basic manifest for a PriorityClass looks like and outline some prerequisites: apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: PRIORITYCLASS_NAME
value: 0 # any integer value between -1000000000 to 1000000000 
description: >-
  (Optional) description goes here!
globalDefault: false # or true. Only one PriorityClass can be the global default. Below are some prerequisites for PriorityClasses: The name of a PriorityClass must be a valid DNS subdomain name. When you make your own PriorityClass, the name should not start with , as those names are
reserved by Kubernetes itself (for example, they are used for two built-in PriorityClasses). system- Its absolute value should be between -1000000000 to 1000000000 (1 billion). Larger numbers are reserved by PriorityClasses such as (this Pod is critically important to the cluster) and (the node
critically relies on this Pod). is a higher priority than , because a
cluster-critical Pod can only work well if the node where it is running has all its node-level
critical requirements met. system-cluster-critical system-node-critical system-node-critical system-cluster-critical There are two optional fields: : When true, this PriorityClass is used for pods where a is not specified.
Only one PriorityClass with set to true can exist in a cluster.If there is no PriorityClass defined with globalDefault set to true, all the pods with no priorityClassName defined will be treated with 0 priority (i.e. the least priority). globalDefault priorityClassName globalDefault : A string with a meaningful value so that people know when to use this PriorityClass. description set to does not mean it will apply the same to the existing pods that are already running. This will be applicable only to the pods that came into existence after the PriorityClass was created. Note: Adding a PriorityClass with globalDefault true PriorityClass in action Here's an example. Next, create some environment-specific PriorityClasses: apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: dev-pc
value: 1000000
globalDefault: false
description: >-
  (Optional) This priority class should only be used for all development pods.
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: preprod-pc
value: 2000000
globalDefault: false
description: >-
  (Optional) This priority class should only be used for all preprod pods. apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: prod-pc
value: 4000000
globalDefault: false
description: >-
  (Optional) This priority class should only be used for all prod pods. Use command to create a pc and to check its status. kubectl create -f <FILE.YAML> kubectl get pc $ kubectl get pc
NAME                      VALUE        GLOBAL-DEFAULT   AGE
dev-pc                    1000000      false            3m13s
preprod-pc                2000000      false            2m3s
prod-pc                   4000000      false            7s
system-cluster-critical   2000000000   false            82m
system-node-critical      2000001000   false            82m The new PriorityClasses are in place now. A small change is needed in the pod manifest or pod template (in a ReplicaSet or Deployment). In other words, you need to specify the priority class name at (which is a string value). .spec.priorityClassName First update the previous production pod manifest file to have a PriorityClass assigned, then delete the Production pod and recreate it. You can't edit the priority class for a Pod that already exists. In my cluster, when I tried this, here's what happened. First, that change seems successful; the status of pods has been updated: $ kubectl get pods --show-labels
NAME            READY   STATUS    	RESTARTS   AGE   LABELS
dev-nginx       1/1     Terminating	0          55s   env=dev
preprod-nginx   1/1     Running   	0          55s   env=preprod
prod-nginx      0/1     Pending   	0          55s   env=prod The dev-nginx pod is getting terminated. Once that is successfully terminated and there are enough resources for the prod pod, the control plane can schedule the prod pod: Warning   FailedScheduling   pod/prod-nginx    0/2 nodes are available: 1 Insufficient cpu, 2 Insufficient memory.
Normal    Preempted          pod/dev-nginx     by default/prod-nginx on node node01
Normal    Killing            pod/dev-nginx     Stopping container dev-nginx
Normal    Scheduled          pod/prod-nginx    Successfully assigned default/prod-nginx to node01
Normal    Pulling            pod/prod-nginx    Pulling image "nginx"
Normal    Pulled             pod/prod-nginx    Successfully pulled image "nginx"
Normal    Created            pod/prod-nginx    Created container prod-nginx
Normal    Started            pod/prod-nginx    Started container prod-nginx Enforcement When you set up PriorityClasses, they exist just how you defined them. However, people
(and tools) that make changes to your cluster are free to set any PriorityClass, or to not
set any PriorityClass at all. However, you can use other Kubernetes features to make sure that the priorities you wanted
are actually applied. As an alpha feature, you can define a and a ValidatingAdmissionPolicyBinding so that, for example,
Pods that go into the namespace must use the PriorityClass.
With another ValidatingAdmissionPolicyBinding you ensure that the namespace
uses the PriorityClass, and so on. In cluster, you can enforce similar controls using external projects such as or , through validating admission webhooks. ValidatingAdmissionPolicy prod prod-pc preprod preprod-pc any Kyverno Gatekeeper However you do it, Kubernetes gives you options to make sure that the PriorityClasses are used how you wanted them to be, or perhaps just to users when they pick an unsuitable option. warn Summary The above example and its events show you what this feature of Kubernetes brings to the table, along with several scenarios where you can use this feature. To reiterate, this helps ensure that mission-critical pods are up and available to serve the traffic and, in the case of a resource crunch, determines cluster behavior. It gives you some power to decide the order of scheduling and order of for Pods. Therefore, you need to define the PriorityClasses sensibly. For example, if you have a cluster autoscaler to add nodes on demand, make sure to run it with the PriorityClass. You don't want to get in a situation where the autoscaler has been preempted and there are no new nodes coming online. preemption system-cluster-critical If you have any queries or feedback, feel free to reach out to me on . LinkedIn This post is originally published on . Kubernetes blog

Using PriorityClass to Protect your Mission-Critical Pods from Eviction

Adding end-to-end (E2E) tests to your application from scratch is a lot of work. Building a complete test suite will be a long project, so it makes sense to prioritize wisely which use cases get covered first. In this article, I will demonstrate the testing approach I’ve been using successfully in a project I’ve been working on for the past 8 years. Return on investment Return on investment (ROI) is a measurement used to compare the value of different investments. Usually, it’s defined as: In the case of E2E, we can understand the parts of the equation as follows: Profit—benefits of having a given case covered by tests Cost—how difficult it will be to cover it with tests Higher ROI means better investment. To maximize the positive impact of the test automatization, it makes sense to start with the case that has the highest ROI, meaning ones that are the easiest to test and are the most important features. Low-hanging fruits Especially when your team is new to testing, it’s good to start with the least challenging cases. It will allow for small victories as your team learns how to test the application. Having some simple tests will help you figure out how to include the tests in continuous integration and the workflow of the developers. Smoke tests A perfect example of simple E2E is a smoke test—a test that visits a page and checks if it can be displayed without falling apart. In those tests, I usually only visit the given route and check if the title or some key button of the page is there. Even such a simple test can achieve quite a few things for you: It checks whether the page works—it will fail on CI if you forget to commit some of the necessary changes, or dependencies It forces you to lay the groundwork for a more complicated test: setting up the test user, providing basic data for tests. In my project, I keep a policy of requiring at least the smoke test for every route we create. This helps to keep other developers aware of E2E as well. Bonus: application screenshot If you get the configuration of your E2E right, as a side effect of visiting all the routes, you can get a collection of screenshots of your application. On some occasions, I’ve been using those to see how a given screen looked before I messed up the UI. Critical workflows The next important topic to cover is the critical workflows of the application. This will be the following: main operations of the users in the app: adding to cart and checkout in a webshop getting a booking for an accommodation app etc. user registration and login contact or feedback form All these are things that will greatly impact operation if some regression in them were deployed to the production version. Inside those workflows, we can prioritize as follows: Happy path A user is on a happy path when everything goes as planned: there is enough stock for the product, payment works perfectly, etc. The happy path covers cases when the user is happy and the company makes money—and we definitely want our software to work perfectly in those cases. Important edge cases Some edge cases are so common that they affect the journey of many users. Based on my experience as a consumer, good examples would be mistakes in credit card numbers or rejected transactions. These kinds of critical error cases are a suitable candidate for covering with E2E. Reappearing errors If a bug makes it all the way to the production, it's proof that a team has no sufficient quality assurance process in a given area. Thus, reappearing bugs are a perfect candidate to cover with an additional automated test—no matter how complicated and uncommon a given situation is. I’ve been trying to follow a policy of covering all regressions from production with E2E, and my conclusions are it is a good guide for finding cases worth testing, but it takes a lot of discipline. Seasonal features Another important thing to keep in mind are features that are used only occasionally. In the application I work on, we have a set of features related to inventory in shops: something that is used intensively but just during a very short period every year. If your application has a similar important but sporadically used part, it’s worth the extra effort to make sure both the users and your team avoid unpleasant surprises when the season comes. Keep a balance Developing and maintaining a suite of E2E tests is about maintaining a balance and adapting to changing circumstances. Certainly, you need the most important parts of your applications covered, but the relative importance of features can change with time, and your test priority should reflect that as well. Want to learn more? Congratulations on learning about E2E—it’s a great investment for the long-term health of your project and a good way of catching issues before they affect users (and stress your team). Also published . here

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