Lanes

lanes

Authors: (1) William P. Wagner IV, Claremont Graduate University. Table of Links Abstract & Introduction Definition of Key Terms Fundamentals of Internet Operation Encapsulation Usage-Based Economic Models Net Neutrality Legal History Researcher Conclusions Areas for Further Exploration & References 6. Net Neutrality The debate in the U.S. has moved to the courts and is grounded in a few basic concepts. The basics of the debate are not usually contested, it’s the results of any changes and the legal foundations for either preventing or allowing those changes that is cause for debate. Net Neutrality enters the equation when the network providers want to charge based on content. 6.1. Content Based Subscription Packages The National ISPs want to be allowed to charge certain Content Providers more than others at one end, and charge consumers at the other end for access to that same content. In both cases this might be in addition to any usage fees. Those opposed to net neutrality regulation argue that ISPs need to be able to charge both consumers and providers based on content because certain content providers use more network resources than others, putting un-acceptable demands on the ISP to provide network resources [12]. They claim that regulation will stifle competition amongst ISPs, slowing the spread of broadband and increasing costs to consumers. Those in favor of net neutrality legislation argue that the network resources should be allocated and paid for based solely on usage rather than content. Further, they argue that the networks need to remain content neutral in order to ensure true competition, innovation and investment along the “edge” of the internet. Proponents argue that this edge activity is what spurs network growth and user need, not the other way around. 6.2. “Fast lanes” The National ISPs want to be able to throttle the speed or charge extra based on content or content provider to provide “fast lanes.” Opponents of net neutrality legislation argue that the content does not always originate on their network, so they are forced to absorb increased network traffic and allocate resources without seeing any direct benefit. Proponents of net neutrality argue that the ISPs bill their customers based on a usage plan that measures both speed and total data. The consumer is paying for a certain size pipe. The ISP does not refund money when the pipe is un-used, and the consumer cannot exceed the size of the pipe, so why should the ISP charge the consumer more when they maximize their purchased allocation? 6.3. ISP Content Providers Some of the National ISPs are also Content Providers. Opponents of net neutrality argue this has no bearing on the previous two changes. They argue that this will not lead to ISPs increasing prices to their competitors, but instead will offer consumers discounted prices for in-house entertainment. Proponents of net neutrality argue that most of the country is only served by one or two high-speed solutions per location, so allowing an ISP to throttle or surcharge their consumers based on content or creator is monopolistic. Most consumers simply do not have any option to change to. They argue this would lead to less competition and innovation, and higher prices to the consumer for less bandwidth. 6.4. International ISPs The National ISPs want regulation to prevent similar billing structures from the International ISPs down to the National level. The National ISPs argue that National ISPs provide the necessary link to needed customers while International ISPs are basically just networking between National ISPs. The National ISPs argue they provide the crucial link to the consumers of both networking and application services and in many cases they are the sole provider, so the International ISPs need them to reach the consumers. The International ISPs argue they need to absorb network traffic from all National ISPs and that some National ISPs use far more resources than others. Further, they argue that very little of the traffic they carry originates on their network – affording them little opportunity to charge at the source. Proponents of network neutrality in the US would argue that the National ISPs represent themselves as competition to themselves when discussing U.S. Net Neutrality, however they present themselves as the sole provider in many cases when negotiating with the International ISPs. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

The Net Neutrality Debate: Content Charges, Fast Lanes, and ISP Content Providers

Today, we sit down with Michael to unravel the story behind 0G Labs, a company that's not just participating in the Web3 revolution but actively shaping its future. With groundbreaking solutions that promise to deliver unprecedented speed, scalability, and cost-effectiveness, 0G Labs is positioning itself at the forefront of the next generation of blockchain technology. In this exclusive interview, we'll explore the technical innovations that allow 0G to achieve mind-boggling throughputs of 50 GB/second, dive into the architectural decisions that make their solution 100 times more cost-effective than alternatives, and uncover Heinrich's vision for enabling advanced use cases like on-chain AI and high-frequency DeFi. Ishan Pandey: Hello Michael, welcome to our 'Behind the Startup' series. You've had a successful journey with garten, your previous venture in corporate wellbeing. What inspired you to transition from that space to creating 0G Labs, and how does your experience as a founder inform your approach to Web3 and blockchain technology? Michael Heinrich: Thank you for having me. My journey with Garten taught me the importance of resilience and adaptability, especially during the pandemic. Transitioning to 0G Labs was driven by my passion for cutting-edge technology and a realization of the critical needs in Web3’s growing data and AI infrastructure. By collaborating with other bright minds, such as our CTO Ming Wu, we identified the opportunity to address existing gaps. With 0G Labs, we're aiming to make high-performance on-chain needs such as AI a reality. Ishan Pandey: 0G Labs is positioning itself as a leading Web3 infrastructure provider, focusing on modular AI blockchain solutions. Can you explain the core concept behind 0G's data availability system and how it addresses the scalability and security trade-offs in blockchain systems? Michael Heinrich: 0G Labs' core concept revolves around our novel data availability system, designed to address the scalability and security challenges in blockchain technology. Data availability ensures that data is accessible and verifiable by network participants, which is important for a wide range of use cases in Web3. For example, Layer 2 blockchains like Arbitrum handle transactions off-chain and then publish that to Ethereum, where data must be proven as available. And yet, traditional data availability solutions have limitations in terms of throughput and performance and are inadequate for high-performance applications such as on-chain AI. Our approach with 0G DA involves an architecture comprising of 0G Storage, where data is stored, as well as 0G Consensus which confirms as being “available”. A random group of nodes is then selected from 0G Storage and comes to consensus on data being available. To avoid issues in scaling, we can add infinitely more consensus networks, all managed by a shared set of validators through a process called shared staking. This allows us to handle vast amounts of data with high performance and low cost, enabling advanced use cases like on-chain AI, high-frequency DeFi, and more. Ishan Pandey: 0G claims to achieve throughputs of 50 GB/second, which is significantly faster than competitors. Can you dive into the technical details of how your platform achieves this speed, particularly in the context of decentralized nodes scaling issue? Michael Heinrich: One aspect of our architecture that makes us exceptionally fast is that 0G Storage and 0G Consensus are connected through what’s known as our Data Publishing Lane. This is where, as mentioned, groups of storage nodes are asked to come to consensus on data being available. This means they are part of the same system, which speeds things up, but additionally, we partition data into small data chunks and have many different consensus networks all working in parallel. In aggregate, these make 0G the fastest out there by far. Ishan Pandey: Your platform aims to be 100x more cost-effective than alternatives. How does 0G's unique architecture, separating data storage and publishing, contribute to this cost efficiency while maintaining high performance? Michael Heinrich: 0G's architecture significantly enhances cost efficiency by separating data storage and publishing into two distinct lanes: the Data Storage Lane and the Data Publishing Lane. The Data Storage Lane handles large data transfers, while the Data Publishing Lane focuses on verifying data availability. This separation minimizes the workload on each component, reducing the need for extensive resources and allowing for scalable, parallel processing. By employing shared staking and partitioning data into smaller chunks, we achieve high performance and throughput without the cost overhead typical of traditional solutions. This architecture allows us to deliver a platform that is both cost-effective and capable of supporting high-performance applications like on-chain AI and high-frequency DeFi. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Today, we sit down with Michael to unravel the story behind 0G Labs , a company that's not just participating in the Web3 revolution but actively shaping its future. With groundbreaking solutions that promise to deliver unprecedented speed, scalability, and cost-effectiveness, 0G Labs is positioning itself at the forefront of the next generation of blockchain technology. 0G Labs In this exclusive interview, we'll explore the technical innovations that allow 0G to achieve mind-boggling throughputs of 50 GB/second, dive into the architectural decisions that make their solution 100 times more cost-effective than alternatives, and uncover Heinrich's vision for enabling advanced use cases like on-chain AI and high-frequency DeFi. Ishan Pandey: Hello Michael, welcome to our 'Behind the Startup' series. You've had a successful journey with garten, your previous venture in corporate wellbeing. What inspired you to transition from that space to creating 0G Labs, and how does your experience as a founder inform your approach to Web3 and blockchain technology? Ishan Pandey: Hello Michael, welcome to our 'Behind the Startup' series. You've had a successful journey with garten, your previous venture in corporate wellbeing. What inspired you to transition from that space to creating 0G Labs, and how does your experience as a founder inform your approach to Web3 and blockchain technology? Ishan Pandey: Hello Michael, welcome to our 'Behind the Startup' series. You've had a successful journey with garten, your previous venture in corporate wellbeing. What inspired you to transition from that space to creating 0G Labs, and how does your experience as a founder inform your approach to Web3 and blockchain technology? Michael Heinrich : Thank you for having me. My journey with Garten taught me the importance of resilience and adaptability, especially during the pandemic. Transitioning to 0G Labs was driven by my passion for cutting-edge technology and a realization of the critical needs in Web3’s growing data and AI infrastructure. By collaborating with other bright minds, such as our CTO Ming Wu, we identified the opportunity to address existing gaps. With 0G Labs, we're aiming to make high-performance on-chain needs such as AI a reality. Michael Heinrich Ishan Pandey: 0G Labs is positioning itself as a leading Web3 infrastructure provider, focusing on modular AI blockchain solutions. Can you explain the core concept behind 0G's data availability system and how it addresses the scalability and security trade-offs in blockchain systems? Ishan Pandey: 0G Labs is positioning itself as a leading Web3 infrastructure provider, focusing on modular AI blockchain solutions. Can you explain the core concept behind 0G's data availability system and how it addresses the scalability and security trade-offs in blockchain systems? Ishan Pandey: 0G Labs is positioning itself as a leading Web3 infrastructure provider, focusing on modular AI blockchain solutions. Can you explain the core concept behind 0G's data availability system and how it addresses the scalability and security trade-offs in blockchain systems? Michael Heinrich: 0G Labs' core concept revolves around our novel data availability system, designed to address the scalability and security challenges in blockchain technology. Data availability ensures that data is accessible and verifiable by network participants, which is important for a wide range of use cases in Web3. For example, Layer 2 blockchains like Arbitrum handle transactions off-chain and then publish that to Ethereum, where data must be proven as available. And yet, traditional data availability solutions have limitations in terms of throughput and performance and are inadequate for high-performance applications such as on-chain AI. Michael Heinrich: Our approach with 0G DA involves an architecture comprising of 0G Storage, where data is stored, as well as 0G Consensus which confirms as being “available”. A random group of nodes is then selected from 0G Storage and comes to consensus on data being available. To avoid issues in scaling, we can add infinitely more consensus networks, all managed by a shared set of validators through a process called shared staking. This allows us to handle vast amounts of data with high performance and low cost, enabling advanced use cases like on-chain AI, high-frequency DeFi, and more. Ishan Pandey: 0G claims to achieve throughputs of 50 GB/second, which is significantly faster than competitors. Can you dive into the technical details of how your platform achieves this speed, particularly in the context of decentralized nodes scaling issue? Ishan Pandey: 0G claims to achieve throughputs of 50 GB/second, which is significantly faster than competitors. Can you dive into the technical details of how your platform achieves this speed, particularly in the context of decentralized nodes scaling issue? Ishan Pandey: 0G claims to achieve throughputs of 50 GB/second, which is significantly faster than competitors. Can you dive into the technical details of how your platform achieves this speed, particularly in the context of decentralized nodes scaling issue? Michael Heinrich: One aspect of our architecture that makes us exceptionally fast is that 0G Storage and 0G Consensus are connected through what’s known as our Data Publishing Lane. This is where, as mentioned, groups of storage nodes are asked to come to consensus on data being available. This means they are part of the same system, which speeds things up, but additionally, we partition data into small data chunks and have many different consensus networks all working in parallel. In aggregate, these make 0G the fastest out there by far. Michael Heinrich: Ishan Pandey: Your platform aims to be 100x more cost-effective than alternatives. How does 0G's unique architecture, separating data storage and publishing, contribute to this cost efficiency while maintaining high performance? Ishan Pandey: Your platform aims to be 100x more cost-effective than alternatives. How does 0G's unique architecture, separating data storage and publishing, contribute to this cost efficiency while maintaining high performance? Ishan Pandey: Your platform aims to be 100x more cost-effective than alternatives. How does 0G's unique architecture, separating data storage and publishing, contribute to this cost efficiency while maintaining high performance? Michael Heinrich: 0G's architecture significantly enhances cost efficiency by separating data storage and publishing into two distinct lanes: the Data Storage Lane and the Data Publishing Lane. The Data Storage Lane handles large data transfers, while the Data Publishing Lane focuses on verifying data availability. This separation minimizes the workload on each component, reducing the need for extensive resources and allowing for scalable, parallel processing. By employing shared staking and partitioning data into smaller chunks, we achieve high performance and throughput without the cost overhead typical of traditional solutions. This architecture allows us to deliver a platform that is both cost-effective and capable of supporting high-performance applications like on-chain AI and high-frequency DeFi. Michael Heinrich: Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Redefining Data Availability for the Next Generation of Blockchain Applications with 0G Labs

0G ল্যাবগুলির সাথে ব্লকচেইন অ্যাপ্লিকেশনগুলির পরবর্তী প্রজন্মের জন্য ডেটা উপলব্ধতা পুনরায় সংজ্ঞায়িত করা

Neudefinition der Datenverfügbarkeit für die nächste Generation von Blockchain-Anwendungen mit 0G Labs

Redefiniendo la disponibilidad de datos para la próxima generación de aplicaciones Blockchain con 0G Labs

Redéfinir la disponibilité des données pour la prochaine génération d'applications Blockchain avec 0G Labs

0G लैब्स के साथ ब्लॉकचेन अनुप्रयोगों की अगली पीढ़ी के लिए डेटा उपलब्धता को पुनर्परिभाषित करना

0G Labs による次世代ブロックチェーン アプリケーションのデータ可用性の再定義

0G Labs를 통해 차세대 블록체인 애플리케이션을 위한 데이터 가용성 재정의

Redefinindo a disponibilidade de dados para a próxima geração de aplicações Blockchain com 0G Labs

Переосмысление доступности данных для следующего поколения блокчейн-приложений с помощью 0G Labs

0G Laboratuvarları ile Yeni Nesil Blockchain Uygulamaları için Veri Kullanılabilirliğini Yeniden Tanımlamak

Xác định lại tính khả dụng của dữ liệu cho thế hệ ứng dụng Blockchain tiếp theo với Phòng thí nghiệm 0G

利用 0G Labs 重新定义下一代区块链应用的数据可用性

The Anti-Luck, Decision Making Framework: Mastering the Art of Making Smart Choices

ভাগ্য বিরোধী, সিদ্ধান্ত নেওয়ার কাঠামো: স্মার্ট পছন্দ করার শিল্পে দক্ষতা অর্জন

Das Anti-Glück-Framework zur Entscheidungsfindung: Die Kunst meistern, kluge Entscheidungen zu treffen

El marco de toma de decisiones anti-suerte: dominar el arte de tomar decisiones inteligentes

Le cadre de prise de décision anti-chance : maîtriser l’art de faire des choix intelligents

भाग्य-विरोधी, निर्णय लेने का ढाँचा: स्मार्ट विकल्प बनाने की कला में निपुणता

運に左右されない意思決定フレームワーク: 賢い選択をする技術を習得する

행운 방지, 의사 결정 프레임워크: 현명한 선택을 하는 기술 익히기

A estrutura anti-sorte para tomada de decisões: dominando a arte de fazer escolhas inteligentes

Система принятия решений против удачи: освоение искусства принятия разумных решений

Şans Karşıtı Karar Verme Çerçevesi: Akıllı Seçimler Yapma Sanatında Ustalaşmak

Khung ra quyết định, chống may mắn: Nắm vững nghệ thuật đưa ra lựa chọn thông minh

反运气决策框架：掌握做出明智选择的艺术

Designed for developers at the intersection of curiosity and professional growth, this primer aims to illuminate how asynchronous programming can elevate application performance, scalability, and responsiveness. By focusing on the universal principles of non-blocking operations and event loops, we venture beyond specific technologies like AsyncIO, Node.js, or Go. This exploration is crafted for software developers eager to grasp the efficiencies driving modern software without the complexity of technical jargon. Think of how a single thread handling requests is like a single-lane road in a busy city: each request is a car, and when there's only one lane, cars line up, causing delays. But what if our city — our service — could manage traffic more cleverly? That's where the magic of an asynchronous runtime comes into play. It's like adding an intelligent traffic system to our city, guiding cars through multiple lanes and intersections without waiting. This system keeps traffic flowing smoothly, ensuring no car (or task) waits too long, which is precisely how an asynchronous runtime keeps our software running efficiently. Let's consider an example of a service that completes I/O operations synchronously. For clarity, these operations are shown outside the main flow of execution in the diagram: Blocking I/O during your service's startup phase might be acceptable, but avoiding this approach when processing external requests is advisable. The following diagram illustrates how service efficiency can be enhanced through the adoption of non-blocking I/O operations: These examples highlight scenarios where the server's performance gains are maximized. Nevertheless, the advantage of non-blocking operations persists across any frequency of incoming requests. Even in less-than-ideal conditions, performance benefits from integrating dedicated I/O threads into the processing workflow. The total time required to process a request (from the client's initial request to the final response, as depicted by the blue number on the right) will invariably decrease, provided there are sufficient threads to handle all requests. In the worst-case scenario, this duration will not surpass that of synchronous processing methods. We then encounter a practice that might seem counterintuitive to many developers. When I/O operations constitute a significant portion of request processing time, optimizing other code segments may yield slight improvement. The duration to fetch data from a cache might align closely with the time dedicated to business logic and template rendering. Employing caching or an in-process database can reduce data retrieval times compared to other processing activities. To segment code effectively and facilitate callback execution, one can instruct the runtime to proceed to the next event loop cycle. Below is an illustrative example of how this concept is applied: // blocking callbacks
function func1_cb(str, cb) {
 var res = func1(str);
 cb(res);
}

function func2_cb(str, cb) {
 var res = func2(str);
 cb(res);
}

// non-blocking callbacks
function func1_cb(str, cb) {
 var res = func1(str);
 process.nextTick(function () {
   cb(res);
 });
}

function func2_cb(str, cb) {
 var res = func2(str);
 process.nextTick(function () {
   cb(res);
 });
}

// usage example
func1_cb(content, function (str) {
 func2_cb(str, function (result) {
   // work with result
 });
}); By adopting this methodology to split two parts of calculations, the overall processing time for scenarios with nearly concurrent request arrivals remains the same. However, the response to the first request is delayed: This scenario represents the least favorable outcome when employing the “next tick” strategy. As shown in the initial example, the “next tick” method proves benign for infrequent requests. Should requests arrive at a moderate pace, leveraging this technique enhances processing speed by allowing the initiation of new requests and the start of non-blocking operations to be interleaved during execution breaks. This approach effectively reduces both the total processing time and the average time per request: In conclusion, adopting non-blocking I/O is crucial for enhancing application performance and beneficial in environments with both sparse and heavy incoming request volumes. Additionally, effectively sequencing the execution flow — illustrated by concepts similar to the "next tick" technique — significantly improves server efficiency. Embracing these asynchronous programming practices offers clear advantages over traditional, synchronous methods.

Asynchronous Runtimes: A Primer for the Perplexed Developer

Authors: (1) William P. Wagner IV, Claremont Graduate University. Table of Links Abstract & Introduction Definition of Key Terms Fundamentals of Internet Operation Encapsulation Usage-Based Economic Models Net Neutrality Legal History Researcher Conclusions Areas for Further Exploration & References Abstract This paper serves as a brief technical examination of Net Neutrality and the Internet fundamentals relevant to the discussion. This document seeks to provide sufficient technical perspective that it may inform the political and economic debate surrounding the issue in the United States. Further, this research demonstrates that existing Internet economics are based strictly on usage, and that this model can account for all uses. Finally, I will argue that there should be some legislation and regulation of ISPs with regard to Net Neutrality in the U.S. Keywords - Net Neutrality, Internet, ISP, Provider, Government Regulation 1. Introduction Net Neutrality has been in the news over the last decade as a topic of political, economic, and civil rights consideration in the United States [1][2][3]. As documented in the rest of this paper, The U.S. Government as well as those of other nations [4], state and local governments, the national private networks that make up the Internet backbone, and major technology and content providers are all engaged in an active push-pull to define internet standards that protect innovation, corporate interests, and consumer needs. The Internet is fundamental to success in the modern world [5]. Communities, law enforcement, hospitals, militaries, corporations, and individuals with a certain level of access to the internet are all demonstrably better equipped for success in the current United States and globally. In 1996, in section 230(b) of the Communications Act of 1934, as amended, Congress describes its national Internet policy. Specifically, Congress states that it is the policy of the United States “to preserve the vibrant and competitive free market that presently exists for the Internet” [6] and “to promote the continued development of the Internet [7].” Because of this need, it has become desirable for infrastructure planning and budgeting to define minimum standards. What those minimum standards are and what mechanism should regulate them is the first piece of the Net Neutrality debate. Proponents of Net Neutrality want legislation that prevents ISPs from restricting or prioritizing traffic – known as “fast lanes” – claiming this will stifle freemarket innovation, investment, expansion, and user choice along the “Edge” of the Internet. Opponents of Net Neutrality claim the legislation will stifle freemarket innovation and expansion by ISPs. As illustrated in fig.1, the consumer’s interests are protected through a combination of free enterprise and legislation, so this paper explores any relevant technical considerations and existing protocols, before moving to the economic models, then finally the legal fight in the courts. This research paper demonstrates how all Internet traffic uses a process known as Encapsulation to achieve complete separation between the content/application developers, and the hardware/networking engineers. Finally, this paper explains how the current economic model, based strictly on usage, accounts for all internet traffic down to the smallest possible unit of measure – a single bit. It is beyond the scope of this paper to fully explore the socio-political and economic ramifications of any changes to the current system such as supply-side assistance that would encourage network expansion vs. demand-side stimulus to reduce consumer cost, however this researcher will provide some legal history and make some observations on the current state of the political and legal debate. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

A Technical Perspective on Net Neutrality

নেট নিরপেক্ষতার উপর একটি প্রযুক্তিগত দৃষ্টিকোণ

Eine technische Perspektive zur Netzneutralität

Una perspectiva técnica sobre la neutralidad de la red

Une perspective technique sur la neutralité du Net

नेट न्यूट्रैलिटी पर एक तकनीकी परिप्रेक्ष्य

ネットの中立性に関する技術的観点

망 중립성에 대한 기술적 관점

Uma perspectiva técnica sobre a neutralidade da rede

Технический взгляд на сетевой нейтралитет

Ağ Tarafsızlığına Teknik Bir Bakış

Quan điểm kỹ thuật về tính trung lập ròng

网络中立性的技术视角

102 Stories To Learn About Scrum

268 Stories To Learn About Project Management

266 Stories To Learn About Product Development

Let's learn about via these 15 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Kanban /Learn Repo 1. The Top 10 Books on DevOps You Need to Read I’ve been doing DevOps for a few years now, and I think I’m pretty good at it. Over these years I’ve read some books that really helped me along the way, here they are. 2. Say Goodbye to Scrum and Kanban: Welcome 6-week Product Cycle Up until pretty recently my company’s tech team managed its projects through a Kanban-board. In practice this entailed that we’d come up with a product roadmap for the 12 months ahead and would start working on this from top to bottom, moving tickets through the usual swimming lanes as we went. 3. A Common Misconception About Agile Methodology I have been involved recently in Agile transformation, i went through lots of conversations, with Techy people, that have a lot of misconceptions about Agile. 4. How to Design a Kanban Board Some of the popular implementations of a Kanban board are the following: Trello, JIRA, Microsoft Planner, and Asana. The changes on the board are synchronized i 5. Using Kanban to Work With Teams Without Sprints Working with a team without sprints? Yes, it is real. To know how, read the article. 6. Scrum vs. Kanban: Which is Better for Your Team? Scrum vs Kanban: core principles, similarities and differences. See schedule and cadence in both methods, release approaches, roles and attitude towards changes 7. Product manager dead after ‘taking a step back’ off cliff 8. How to Use The Vue Draggable Library to Create an Interactive Kanban Board Vue Draggable is a great library for Vue.js that makes it super simple to create interactive draggable components with virtually no effort 9. Top 6 Software Development Methodologies of 2022 Here are the top 6 software development methodologies that the project manager and the team must follow throughout the software development process. 10. A Kanban Board Component for Project Management Project management is a crucial domain in the sphere of IT. 11. Significant Updates to Popular Kanban Software in 2019 How Jira, Hygger, Asana, and Others Surprised Their Users in 2019? 12. Agile vs Waterfall: How To Choose The Right Methodology for Your Project Picking the right method for managing your tasks can either make or break the success of your projects. 13. Kanban Vs. Scrum: Here's What Your Team Needs To Know Scrum is the most popular Agile framework today (56% of all Agile teams use Scrum). 14. How to stop prioritizing and start working It is believed that product managers should help a company do something that makes sense and advise against doing something that doesn’t. 15. The Ultimate Project Management Career Guide for 2020 From the desk of brilliant weirdo #1: “What does a project manager really do?” Maybe that’s a question you’ve been asking yourself or others if you have been considering a PM career for a while. To be honest, it’s gonna be quite hard to put all the PM roles under one roof as project managers in one industry can differ from PMs in another. But I’ll do my best to give you a clear image of what it looks like to be a project manager. Thank you for checking out the 15 most read stories about Kanban on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

15 Stories To Learn About Kanban

In the early days, you could mine Bitcoin with your home computer. Miners who were able to hold onto their financial rewards are worth millions. Today, your chances of finding a block solo are . That's like winning the lottery ten times in a row. The odds of making a profit are tiny. roughly 1 in 57.6 trillion Most mining happens in warehouses or mining farms, not home computers or laptops. Large mining farms with specialized ASIC (Application-Specific Integrated Circuit) miners dominate the network. These ASICs can be 100,000 times more efficient than CPUs. Large-scale operations are squeezing out individual miners in the current crypto landscape. Buckle up as we explore GoMining and NFTs. These disruptive technologies are shaping tomorrow's Bitcoin mining landscape. It seems opportunity is knocking, So how can crypto enthusiasts and investors get in on the crypto-mining action? Get Ready To Discover the history of Bitcoin mining. Unravel the mystery of Proof-of-Work. Meet GoMining, the innovative platform democratizing Bitcoin mining with LBH. Learn how NFTs are changing the game. Take action and put mining power in your hands (and your wallet). This is your chance to ditch the outdated and embrace the future. Whether you're a seasoned miner or a curious newcomer, grab your virtual pickaxe. It's time to dig into the exciting world of Bitcoin mining 2.0! Disclaimer: Your mileage may vary, but the potential is definitely there. Unraveling the History of Bitcoin Mining Bitcoin mining started in 2009 with the Genesis Block mined by Satoshi Nakamoto. This was exciting because it introduced a decentralized way to confirm transactions. This power forever changed the face of monetary practices. Mining and confirming transactions in the world of cryptocurrency have a magical quality. They prevent the risk of spending the same money twice. This essential function is the key to the Bitcoin protocol. It paved the way for what we call "trustless transactions." With blockchain and Bitcoin, you don't need a third party to confirm a transaction. Imagine you're a merchant in Singapore who wants to sell a product or service to a customer in San Francisco. You've never met. How do you know if his money is good? You need a third party, like a bank or credit card company to confirm the funds are available and real. Bitcoin removes this need because the transaction is confirmed on the blockchain. This is a disruptive concept. It changes the way information gets shared and business happens. The buzz around bitcoin and blockchain is all about cutting out the middleman. Bitcoin prices climbed through 2016 to over $900 by the end of the year. In 2017, Bitcoin's price hovered around $1,000 until it broke $2,000 in mid-May and then skyrocketed to $19,345.49 on Dec. 16. The word was out. Mainstream investors, governments, economists, and scientists took notice. The popularity of crypto boomed among tech bros and speculative investors. The hype started attracting more miners into the space. The rapid growth intensified computational requirements. “ ” said the Notorious B.I.G. One problem for Bitcoin was slow transaction speeds. Mo Money Mo Problems, Too many transactions were getting crammed onto the network. It's like driving home in rush hour. Lots of cars on a narrow road at the same time. How can we fix this? Hey, it's code. Software updates allowed for bigger blocks. This is like adding more lanes to the narrow road. And it helped somewhat, but it also created other issues. More complex software needs to run on higher-end hardware. Mining shifts from CPU (Central Processing Units) to GPU (Graphic Processing Units). As demand continued to increase, specific mining solutions emerged. Meet sophisticated ASICs (Application Specific Integrated Circuits). Such advanced hardware enabled faster hash rate processing capabilities. But the progress created huge economic costs. Today mining farms house thousands of ASICs. Their high energy consumption makes mining more resource-intensive. It also increases the impact on the environment. Decrypting the Traditional Mining Process and the Role of Proof-of-Work Bitcoin mining requires dedicated physical machinery. ASICs operate around the clock to decipher cryptographic puzzles. These chips form the brains of the operation, designed only for Bitcoin mining. They deliver unmatched hashing power and efficiency compared to general-purpose hardware. And their energy consumption is unbelievable. What is a Bitcoin Mining Rig? Imagine a high-powered computer, humming away in a room with very low light. Your eyes strain to see more than the flashing blue, red, and green indicators. Fans are whirring. This isn't your average gaming computer. It's a bitcoin mining rig. This specialized machine has one purpose. Cracking complex math problems at lightning speed. These rigs are the workhorses of the Bitcoin network. They compete against each other, racing to solve these problems first. The winner gets a juicy reward: a block of bitcoins. A Bitcoin mining rig requires immense processing power. Where are mining farms located? Think of areas where climates are cool and electricity costs are low. What Is Proof-of-Work? The technical magic fueling Bitcoin’s operation is the “Proof-of-Work” consensus algorithm. It creates an environment where participants compete to solve complex puzzles. The rules are specific. They make altering transactions confirmed earlier almost impossible. The first miner to crack the puzzle gets rewarded with minted bitcoins. Plus, they snag the accruing transaction fees. But change is coming for miners. The current block reward of 6.25 bitcoin is being chopped in half in April 2024. This will impact the wallets of miners worldwide. Remember, these rewards play a major role in their profitability. It's like the cherry on top of their mining sundae. Slicing it in half impacts how they can mine that digital gold. Exploring Innovations: Introducing the GoMining Platform The crypto world is always expanding. Use cases matter a lot. The GoMining platform distinguishes itself by redefining Bitcoin mining mechanisms and practices. They use their own powerful computers to mine bitcoins. Then they divide up all the daily mining rewards into NFTs. By representing specific amounts of bitcoin via NFTs, users can trade or hold them. The company has already mined 38,000 BTC. GoMining has nine data centers across the world that mine Bitcoin. Since 2017, they have been building their operations and growing. Their website shows they have 18,911 NFT holders and sold 48,799 NFTs. This is an innovative project. The focus is on user experience, energy efficiency, and environmental sustainability. Plus, it offers a unique passive income opportunity for investors globally. The GOMINING token is what fuels the GoMining economy. Like gas in a car, GOMINING “powers” GoMining’s NFTs, governance processes, and rewards distribution systems. GoMining brings you tradable LBH NFTs. This opens the door to liquid bitcoin hash rate and the benefits of virtual assets with a bonus. Harnessing the Power of Liquid Bitcoin Hashrate (LBH) Tokens LBH tokens increase the democratization of the cryptocurrency space. These innovative developments are opening up a . new avenue within cryptocurrency investments Imagine being a new crypto enthusiast. Your eyes gleam with at the ever-evolving blockchain world. You've heard of bitcoin mining. But you feel locked out. excitement Remember those powerful mining rigs? They’re out of your reach. It looks like your Bitcoin mining opportunity is floating in a galaxy far away. But wait. Here’s the good news. LBH tokens are here to break down that barrier. You've found a secret backdoor into this lucrative opportunity. LBH tokens can be bought, sold, and, due to its properties, allow holders to mine bitcoin every day. Unlike traditional mining, you don't need to do anything at all for this. Forget grappling with complex setups or investing in expensive hardware. LBH tokens act like little digital mining licenses. Hold them? Now, you're renting out a share of a high-powered mining rig without lifting a finger. Think of it like joining a mining co-op. You pool your resources with others. Then a dedicated team takes care of all the technical heavy lifting. You reap the rewards. Users invest and mine bitcoins without active participation or extensive technical know-how. GoMining redefines what it means to invest in crypto. The company brings BTC mining profits within everyone's reach. GoMining's Revolution in Bitcoin Mining GoMining distinguishes itself by incorporating NFTs and LBH into its operations. There are plenty of cornerstones underpinning its potential. But these innovative technologies make GoMining shine. The company is demystifying the complexities around Bitcoin mining. This means more people can take part in this lucrative industry. It encourages broader participation. It promotes decentralization. GoMining provides an accessible platform for both experienced and novice crypto enthusiasts. The future of the crypto space continues to evolve. Bitcoin mining will see more technological advancements. The spot Bitcoin ETF has increased institutional participation. We can expect a greater demand for environmentally friendly practices. New regulatory frameworks will develop around the world. Now, imagine if everyone has access to these new digital asset opportunities. Without the overwhelming complexity. To get started, visit the GoMining website and . Put your toes in the water, or take a deep dive into the new wave of Bitcoin mining. create a 7-day free NFT

Is Bitcoin Mining Dead? GoMining and NFTs Say "Not So Fast!"

বিটকয়েন মাইনিং কি মৃত? GoMining এবং NFTs বলে "এত দ্রুত নয়!"

Ist Bitcoin-Mining tot? GoMining und NFTs sagen „Nicht so schnell!“

¿Está muerta la minería de Bitcoin? GoMining y NFT dicen "¡No tan rápido!"

Le minage de Bitcoin est-il mort ? GoMining et les NFT disent « Pas si vite ! »

क्या बिटकॉइन माइनिंग ख़त्म हो गई है? गोमाइनिंग और एनएफटी कहते हैं "इतनी जल्दी नहीं!"

ビットコインマイニングは終わったのか？ GoMining と NFT は「それほど速くない!」と言う

비트코인 채굴은 죽었는가? GoMining과 NFT는 "그다지 빠르지 않다!"라고 말합니다.

A mineração de Bitcoin está morta? GoMining e NFTs dizem “Não tão rápido!”

Биткойн-майнинг мертв? GoMining и NFT говорят: «Не так быстро!»

Bitcoin Madenciliği Öldü mü? GoMining ve NFT'ler "O Kadar Hızlı Değil!" Diyor

Khai thác Bitcoin có chết không? GoMining và NFT nói "Không nhanh như vậy!"

比特币挖矿已经死了吗？ GoMining 和 NFT 说“没那么快！”

Semantic segmentation is an area of computer vision that specialises in dividing an image into regions based on pixel characteristics to identify objects or boundaries, simplifying image analysis. The method has many different use cases, from detecting handwritten words and numbers in scanned documents to Google and Apple employing it for foreground-background separation in portrait mode. Platforms like Pinterest and Amazon leverage semantic segmentation for image uploading, enabling the identification of similar-looking products by segmenting the area of interest from the rest. Semantic segmentation is a cornerstone for the autonomous vehicle industry, as it helps identify lanes, cars, and pedestrians. This technology is crucial for self-driving cars to comprehend their surroundings through camera video inputs, enabling informed decisions about future actions. Moreover, semantic segmentation finds application in the field of in vitro fertilisation (IVF), particularly during embryo classification. It is used to precisely delineate embryonic structures, contributing to the accurate assessment of embryo development and selection in assisted reproductive technologies. In this article, I will explore the intricate field of semantic segmentation and explain how to evaluate semantic segmentation efficiency with examples from the embryology field. Core Concepts of Semantic Segmentation Semantic segmentation builds upon image classification models, refining the process by labelling each pixel to a predefined class. Pixels of the same class form a segmentation mask, offering precise object classification and boundary delineation. In this process, an input image undergoes neural network processing, resulting in a colourised feature map where each pixel colour signifies a distinct class label. This spatial understanding allows computers to differentiate objects, separate foreground from background, and automate tasks in robotics. Understanding Semantic Segmentation The semantic segmentation process unfolds in distinct steps: Initiated with an input image, the semantic segmentation model processes it through a complex neural network architecture. Input Image Processing The neural network dissects the image, extracting intricate features and patterns, and understands the visual content in granular detail. Neural Network Analysis Unlike traditional image classification, semantic segmentation operates at the pixel level. Each pixel in the image is individually classified, contributing to the creation of a detailed segmentation mask. Pixel-level Classification The output of the model is a colourised feature map of the image. Each pixel's colour corresponds to a specific class label (usually an integer), distinguishing various objects within the scene. Colorised Feature Map Pixels sharing the same class label are grouped, forming coherent segments in the segmentation mask. It helps to perform the precise identification and localisation of objects. Grouping Pixels The segmented image offers a spatial understanding and makes it possible for computers to discern objects, separate foreground elements from the background, and grasp the contextual relationships between different entities. Spatial Understanding Semantic segmentation, with its pixel-level analysis, provides a foundation for advanced visual comprehension, dramatically changing how machines interpret and interact with visual data. Evaluation Metrics The predominant class of metrics for semantic segmentation falls under the category of overlap metrics. These metrics measure how well the model's prediction matches the actual details in a given image. This provides important information about how accurate the model is. Overlap Metrics The most widely employed overlap metrics include the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), also known as the Jaccard Index. Both metrics provide a numerical representation of the overlap between ground truth and predicted masks, with values ranging from 0 (no overlap) to 1 (full overlap). Though these metrics provide considerable insights, they have limitations as well. Notably, they cannot discern shape differences, a critical factor in scenarios like blastocyst segmentation in the field of embryology, where accurate prediction of non-trivial shapes is essential. Boundary-based metrics can help to identify changes in shapes and complement overlap metrics. However, they also have limitations. Although they can detect shape differences, they often overlook the structural peculiarities of masks, such as holes within predictions. This is particularly significant in the context of blastocyst segmentation, where components may contain holes. Volume-Based Metrics Another dimension to consider in segmentation is volume accuracy. Metrics based on volume are crucial for checking how accurate semantic segmentation models are. They help to measure the size-related details of the predictions. Two key metrics in this category are Volume Similarity (VS) and Relative Volume Difference (RVD). measures the similarity between ground truth and prediction masks by considering their respective volumes. This metric offers a quantitative assessment of how closely the predicted volume aligns with the actual volume. It helps researchers make a more thorough evaluation of the segmentation accuracy. Volume Similarity (VS) quantifies the disparity in volume between the ground truth and prediction masks in relation to the volume of the ground truth mask. This metric provides a normalised measure of the volume difference, offering insights into the proportion of discrepancy relative to the true volume. Relative Volume Difference (RVD) Volume-based metrics are an effective way to evaluate the performance of semantic models, especially in situations where precise volume prediction is crucial. One major drawback of using such metrics is that they do not consider the location of the predicted mask. As a result, relying solely on them may result in predictions of the correct size but in the wrong place. To address this issue, overlap or boundary metrics should be integrated into the evaluation process. When evaluating models, it is crucial to select relevant metrics. Although boundary-based, and volume-based metrics can be useful in certain scenarios, they don't consider all of the intricacies of mask structure. The Dice Similarity Coefficient (DSC) is the most versatile and commonly used metric for semantic segmentation evaluation and is recommended as a go-to solution. This choice is in line with the trends observed in state-of-the-art models that focus on blastocyst semantic segmentation. [1] [2] [3] Challenges After summarising my experience in analysing semantic segmentation evaluation metrics, it is clear that there are several challenges to accurately assessing the performance of segmentation models. These challenges are mainly related to shape variability, hole recognition, and nuanced volume accuracy measurement. In the following insights, I will outline these key difficulties encountered: Existing metrics often struggle to accurately evaluate shape variability, particularly for objects with intricate or non-trivial structures. Capturing diverse shapes, which is utterly important in medical imaging applications or object recognition, becomes a challenging task. Shape Variability Assessment. Boundary-based metrics commonly used in evaluation may not effectively recognise holes within segmented objects. This limitation impacts the metric's ability to comprehensively assess the structure of objects, especially when internal voids are present. Hole Recognition Limitations. While volume-based metrics provide insights into volumetric aspects, accurately predicting the volume of each segmented component remains challenging. Relying solely on volume metrics may lead to incomplete evaluations, particularly in scenarios where precise volume prediction is essential. Volume Accuracy Measurement. Evaluating spatial relationships and contextual understanding within the segmented areas is a complex task. Existing metrics may struggle to capture the intricate interplay of objects and their surroundings, limiting the holistic assessment of segmentation quality. Difficulty in Addressing Spatial Relationships. Metrics may face difficulty distinguishing between similar structures, leading to potential misclassification or underestimation of segmentation accuracy. This becomes pronounced in scenarios where objects share visual similarities but have distinct semantic meanings. Inability to Differentiate Similar Structures. Evaluation metrics may lack adaptability to specific domains or applications. Thus, to ensure relevant and accurate assessments, it is crucial to fine-tune metrics for domain-specific nuances. Limited Adaptability to Domain-Specific Requirements. The outlined challenges signify a dynamic scientific landscape that calls for continual refinement in assessment methodologies. Semantic segmentation is a crucial technology that has wide-ranging applications in areas such as e-commerce, medical imaging, and autonomous vehicles. Its potential to improve people's lives is significant. However, to use it efficiently, we need to be able to measure its accuracy, and evaluating the accuracy remains challenging. Metrics such as the Dice Similarity Coefficient and Intersection over Union are not perfect, as they struggle with shape variability and hole recognition. Therefore, a deeper understanding and improvement of these evaluation metrics is essential. By combining overlap, boundary-based, and volume-based metrics, we can enhance the accuracy of semantic segmentation. This advancement will bring notable benefits to various industries.

A Deep Dive Into Semantic Segmentation Evaluation Metrics

Scaling a SaaS product is not just about growth. Instead, it is a complex form of art that requires precision, expertise, and a deep understanding of the constantly changing tech ecosystem. This guide helps entrepreneurs, product managers, and tech enthusiasts navigate the complex journey of scaling a experience. SaaS Paul Dhaliwal's vast knowledge and deep insights act like a guide in this complex field. His advice is like a treasure chest of strategies and wisdom to help you bring out the best in your product. Join us on this transformative journey of SaaS scaling, where every section is propelling your product to unprecedented heights. SaaS Scaling Strategies Scaling a SaaS (Software as a Service) product is an important step in its growth journey. It involves strategies and approaches to ensure that your software can handle a growing user base, increasing data loads, and evolving market demands. Key Approaches to Scaling In my journey as the founder of RedBlink Technology, I've learned that scaling a SaaS product demands a strategic approach. One fundamental tactic is vertical scaling, or what we call "scaling up." This involves fortifying our existing infrastructure, enhancing server resources, optimizing databases, and implementing efficient load balancing. It is similar to giving your engine more horsepower. On the flip side, horizontal scaling, or "scaling out," has been essential in our growth. It's like adding more lanes to a highway to adjust more traffic. This strategy allows us to scale more dynamically and withstand server failures more robustly. Yet, it requires careful planning and a solid load-balancing system to function seamlessly. Choosing the Right Strategy Selecting the appropriate scaling strategy depends on multiple factors, like the nature of your SaaS product, budget constraints, and anticipated growth patterns. It is essential to conduct a thorough analysis of your current infrastructure and projected user base to make an informed decision. SaaS Growth Tactics The SaaS growth tactics are built on precision. We excel in targeted marketing, crafting campaigns that resonate with potential customers' needs. A seamless user onboarding experience accelerates understanding and engagement, cultivating lasting user relationships and sustaining our growth. Effective Techniques for Expansion One thing that we all must keep in mind is that expanding a SaaS product is not just about attracting new users but also about retaining existing ones. It's a delicate balance that requires a strategic approach. undefined Understanding our target audience and crafting personalized marketing messages has been instrumental. By addressing specific pain points and demonstrating how our SaaS product meets those needs, we've been able to attract users who are genuinely interested in what we offer. [ ] Targeted Marketing Campaigns: undefined The initial experience a user has with our product is crucial. We've invested time and resources into streamlining the onboarding process, ensuring that new users quickly grasp the value and functionality of our software. Clear guidance and resources play a pivotal role in this process. [ ] User Onboarding Optimization: Leveraging Market Trends To keep growing, it's vital to stay up-to-date. Make sure to always pay close attention to new trends and technologies that are coming up. This means we're always on the lookout for what's next! Understanding how user behavior evolves allows us to tailor our product to meet shifting preferences and expectations. This adaptability keeps us relevant in a fast-paced market. Adapting to User Behavior Changes: Integrating new technologies that align with our product's mission has been a key strategy. Whether it's incorporating AI capabilities or adopting new development frameworks like Code Conductor, staying at the forefront of technology ensures that our SaaS product remains competitive. Embracing Emerging Technologies: Scaling Software as a Service Our approach to scaling Software as a Service (SaaS) revolves around two pivotal principles. , we prioritize the unwavering reliability of our SaaS product, ensuring that it remains available and resilient even in the face of unexpected challenges. First and foremost This commitment is underscored by the implementation of redundancy measures, robust failover systems, and stringent testing protocols. Additionally, we adhere to a foundational design philosophy of scalability. This design approach embraces modularity, harnesses the power of microservices, and utilizes scalable databases, ensuring that our SaaS solution can seamlessly expand to meet the demands of a growing user base and evolving market dynamics. SaaS Product Expansion Tips and Scalability Challenges We've proven methods for expanding our SaaS product. This involves feature diversification, thorough market research, strategic partnerships, localized and global marketing, and integrating user feedback. However, scaling isn't without challenges. We've tackled issues like database limitations, maintaining user experience, cost management, security, disaster planning, and compliance adherence. By prioritizing risk mitigation through security protocols, disaster recovery plans, and compliance measures, we ensure the integrity and continuity of our SaaS product. These strategies collectively underpin our success, providing valuable guidance for those navigating the complexities of scaling a SaaS product. Best Practices for Scaling SaaS We adhere to industry-tested recommendations for achieving sustainable growth in the SaaS sector. These practices are the bedrock of our success: Placing the customer at the center of our operations ensures that our product evolves in ways that truly benefit users. Customer-Centric Approach: This is the second practice where embracing a cycle of continuous improvement allows us to respond swiftly to user feedback and evolving market trends. Iterative Development: Building a robust technical foundation that can flexibly accommodate increased demands is vital for long-term success. Scalable Infrastructure: Upholding the highest standards of security and compliance safeguards not only our product but also builds trust with users. Compliance and Security: Investing in our team's skills and capabilities ensures that we're equipped to tackle new challenges and opportunities. Talent and Team Growth: Staying attuned to industry shifts and user needs allows us to pivot and adapt our product strategy for sustained relevance. Market Adaptability: By adhering to these best practices, we've not only achieved consistent growth but also built a foundation for sustainable success in the competitive landscape of the SaaS industry. These recommendations serve as a guiding light for others seeking to scale their own SaaS products effectively. Conclusion All-in-all, I can say that scaling a SaaS product is about empowering lives, streamlining processes, and redefining what's possible. It's not just growth; it's evolution. Embrace challenges, dream big, and stay true to your vision. Together, we're shaping a future limited only by imagination. As I reflect on our journey at RedBlink Technology, I'm reminded of the entrepreneurial insights we've gained along the way, particularly in leveraging AI for startup success. It's not about adopting the latest buzzwords; it's about harnessing the potential of cutting-edge technologies to create solutions that truly matter. AI has been our ally in crafting smarter, more efficient SaaS products, and I encourage fellow entrepreneurs to explore its boundless possibilities.

How to Scale a SaaS Product From a Founder's Perspective

Scientific American Supplement, No. 711, August 17, 1889, by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . GIBRALTAR. here GIBRALTAR. The point or rock known as Gibraltar is a promontory two and one-half miles long and from a quarter to three-quarters of a mile wide. It rises abruptly from the sandy shore to a height at its highest point of 1,408 ft. It is composed of gray limestone, honeycombed with caves and subterranean passages, some of which contain most beautiful stalactites in the form of massive pillars. Gibraltar is emphatically a fortress, and in some respects its fortifications are unique. On the eastern side the rock needs no defense beyond its own precipitous cliffs, and in all other directions it has been rendered practically impregnable. Besides a sea wall extending at intervals round the western base of the rock, and strengthened by curtains and bastions and three formidable forts, there are batteries in all available positions from the sea wall up to the summit, 1,350 feet above the sea, and a remarkable series of galleries has been hewn out of the solid face of the rock toward the north and northwest. These galleries have an aggregate length of between two and three miles, and their breadth is sufficient to let a carriage pass. Portholes are cut at intervals of twelve yards, so contrived that the gunners are safe from the shot of any possible assailants. At the end of one of the galleries hollowed out in a prominent part of the cliff is St. George's Hall, 50 feet long by 85 feet wide, in which the governor was accustomed to give fetes. Alterations, extensions, and improvements are continually taking place in the defensive system, and new guns of the most formidable sort are gradually displacing or supplementing the old fashioned ordnance. The whole population of Gibraltar, whether civil or military, is subjected to certain stringent rules. For even a day's sojourn the alien must obtain a pass from the town major, and if he wish to remain longer, a consul or householder must become security for his good behavior. Licenses of residence are granted only for short periods—ten, fifteen, or twenty days—but they can be renewed if occasion require. Military officers may introduce a stranger for thirty days. A special permit is necessary if the visitor wishes to sketch. Though the town of Gibraltar may be said to date from the fourteenth century, it has preserved very little architectural evidence of its antiquity. Rebuilt on an enlarged and improved plan after its almost complete destruction during the great siege, it is still, on the whole, a mean-looking town, with narrow streets and lanes and an incongruous mixture of houses after the English and the Spanish types. As a proprietor may at any moment be called upon to give up his house and ground at the demand of the military authorities, he is naturally deterred from spending his money on substantial or sumptuous erections. The area of the town is about one hundred acres. Gibraltar was known to the Greek and Roman geographers as Calpe or Alybe, the two names being probably corruptions of the same local (perhaps Phenician) word. The eminence on the African coast near Ceuta, which bears the modern English name of Apes' Hill, was then designated Abyla; and Calpe and Abyla, at least according to an ancient and widely current interpretation, formed the renowned pillars of Hercules (Herculis columnæ), which for centuries were the limits of enterprise to the seafaring peoples of the Mediterranean world. The strategic importance of the rock appears to have been first discovered by the Moors, who, when they crossed over from Africa in the eighth century, selected it as the site of a fortress. From their leader, Tarik Ibn Zeyad, it was called Gebel Tarik or Tarik's Hill; and, though the name had a competitor in Gebel af Futah, or Hill of the Entrance, it gradually gained acceptance, and still remains sufficiently recognizable in the corrupted form of the present day. The first siege of the rock was in 1309, when it was taken by Alonzo Perez de Guzman for Ferdinand IV. of Spain, who, in order to attract inhabitants to the spot, offered an asylum to swindlers, thieves, and murderers, and promised to levy no taxes on the import or export of goods. The attack of Ismail Ben Ferez, in 1315 (second siege), was frustrated; but in 1333 Vasco Paez de Meira, having allowed the fortifications and garrison to decay, was obliged to capitulate to Mahomet IV. (third siege). Alphonso's attempts to recover possession (fourth siege) were futile, though pertinacious and heroic, and he was obliged to content himself with a tribute for the rock from Abdul Melek of Granada; but after his successful attack on Algeciras in 1344 he was encouraged to try his fortune again at Gibraltar. In 1349 he invested the rock, but the siege (fifth siege) was brought to an untimely close by his death from the plague in February, 1350. The next or sixth siege resulted simply in the transference of the coveted position from the hands of the King of Morocco to those of Yussef III. of Granada; and the seventh, undertaken by the Spanish Count of Niebla, Enrico de Guzman, proved fatal to the besieger and his forces. In 1462, however, success attended the efforts of Alphonso de Arcos (eighth siege), and in August the rock passed once more under Christian sway. The Duke of Medina Sidonia, a powerful grandee who had assisted in its capture, was anxious to get possession of the fortress, and though Henry IV. at first managed to maintain the claims of the crown, the duke ultimately made good his ambition by force of arms (ninth siege), and in 1469 the king was constrained to declare his son and his heirs perpetual governors of Gibraltar. In 1479 Ferdinand and Isabella made the second duke Marquis of Gibraltar, and in 1492 the third duke, Don Juan, was reluctantly allowed to retain the fortress. At length, in 1501, Garcilaso de la Vega was ordered to take possession of the place in the king's name, and it was formally incorporated with the domains of the crown. After Ferdinand and Isabella were both dead the duke, Don Juan, tried in 1506 to recover possession, and added a tenth to the list of sieges. Thirty-four years afterward the garrison had to defend itself against a much more formidable attack (eleventh siege)—the pirates of Algiers having determined to recover the rock for Mahomet and themselves. The conflict was severe, but resulted in the repulse of the besiegers. After this the Spaniards made great efforts to strengthen the place, and they succeeded so well that throughout Europe Gibraltar was regarded as impregnable. In the course of the war of the Spanish succession, however, it was taken by a combined English and Dutch fleet under Sir George Rooke, assisted by a body of troops under Prince George of Hesse-Darmstadt. The captors had ostensibly fought in the interests of Charles Archduke of Austria (afterward Charles III.), but, though his sovereignty over the rock was proclaimed on July 24, 1704, Sir George Rooke on his own responsibility caused the English flag to be hoisted, and took possession in name of Queen Anne. It is hardly to the honor of England that it was both unprincipled enough to sanction and ratify the occupation and ungrateful enough to leave unrewarded the general to whose unscrupulous patriotism the acquisition was due. The Spaniards keenly felt the injustice done to them, and the inhabitants of the town of Gibraltar in great numbers abandoned their homes rather than recognize the authority of the invaders. In October, 1704, the rock was invested by sea and land; but the Spanish ships were dispersed by Sir John Leake, and the Marquis of Villadarias fared so ill with his forces that he was replaced by Marshal Tesse, who was at length compelled to raise the siege in April, 1705. During the next twenty years there were endless negotiations for the peaceful surrender of the fortress, and in 1726 the Spaniards again appealed to arms. But the Conde de la Torres, who had the chief command, succeeded no better than his predecessors, and the defense of the garrison under General Clayton and the Earl of Portmore was so effectual that the armistice of June 23 practically put a close to the siege, though two years elapsed before the general pacification ensued. The most memorable siege of Gibraltar, indeed one of the most memorable of all sieges, was that which it sustained from the combined land and sea forces of France and Spain during the years 1779-1783. The grand attack on the place was made on the 13th September, 1782, and all the resources of power and science were exhausted by the assailants in the fruitless attempt. On the side of the sea they brought to bear against the fortress forty-six sail of the line and a countless fleet of gun and mortar boats. But their chief hope lay in the floating batteries planned by D'Arcon, an eminent French engineer, and built at the cost of half a million sterling. They were so constructed as to be impenetrable by the red hot shot which it was foreseen the garrison would employ; and such hopes were entertained of their efficiency that they were styled invincible. The Count D'Artois (afterward Charles X.) hastened from Paris to witness the capture of the place. He arrived in time to see the total destruction of the floating batteries and a considerable portion of the combined fleet by the English fire. Despite this disaster, however, the siege continued till brought to a close by the general pacification, February 2, 1783. The history of the four eventful years' siege is fully detailed in the work of Drinkwater, who himself took part in the defense, and in the life of its gallant defender Sir George Augustus Eliott, afterward Lord Heathfield, whose military skill and moral courage place him among the best soldiers and noblest men whom Europe produced during the 18th century. Since 1783 the history of Gibraltar has been comparatively uneventful. In the beginning of 1801 there were rumors of a Spanish and French attack, but the Spanish ships were defeated off Algeciras in June by Admiral Saumarez. Improvements in the fortifications, maintenance of military discipline, and legislation in regard to trade and smuggling are the principal matters of recent interest. 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. Various (2004). Scientific American Supplement, No. 711, August 17, 1889. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/16972/pg16972-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

The point or rock known as Gibraltar is a promontory two and one-half miles long

Scientific American, Volume XXXVI., No. 8, February 24, 1877 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Natural Ornaments in Winter. here Natural Ornaments in Winter. Now that the hedges are no longer green, and the trees stand black and bare on the landscape, is the time to seek for endless variety and beauty waiting to be admired in its turn. What miniature fairy glens and grottoes are distributed over the hedge banks of our country lanes! Mosses, delicate and beautiful, may be found in the interstices of any old wall, or at the foot of almost any tree or shrub. In the winter time mosses and lichens are found in fruit, and are beautiful objects. A pocket microscope lens is essential for their proper observation; and though the delicate carmine cups of the species known as the cup moss, and the familiar gray and yellow mosaic appearance we see on twigs and branches on our way, are easily recognized, the study of this form of winter vegetation is an inexhaustible one, and is an occupation for a lifetime, if earnestly pursued. We do not however, suggest that every one who endeavors to recognize the different species of moss, lichens, or fungi should necessarily do so through the medium of the microscope; but it will greatly add to the pleasure of making a collection out of doors if there be a good microscope at home, so that when the contents of the basket be turned out, after the winter's walk, there should be interest even in the fragments left, after a little pile of varied bits has been constructed, rivalling the choicest summer bouquet in beauty of form and color. We have seen such a collection formed into a beautiful object by raising a little mound of rough bits of bark in a plate or saucer, and placing on it varieties of fungus of every shade of red, brown, yellow, and gray. They seem to spring forth from a bed of sphagnum or bog moss of brightest emerald green; while a clump of the screw wall moss in fruit, with its curious little box-like capsules, supports a gray or yellow lichen, which has been gently removed from some old wall or tree. A bit of stick or a twig, incrusted with a bright orange-colored lichen, supports a trailing branch of delicate green ivy, the most beautiful and adaptable of all winter foliage. Over this little arrangement is placed a bell glass, to preserve it from dust and the effect of a dry atmosphere; and we know how pleasing to the eye is its varied beauty of form and color, lasting thus, a constant source of pleasure, for many a day without renewal.— . Chambers' Journal 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. Various (2006). Scientific American, Volume XXXVI., No. 8, February 24, 1877. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19406/pg19406-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at www.gutenberg.org https://www.gutenberg.org/policy/license.html

Natural Ornaments in Winter

Blue-collar jobs have already been impacted heavily by robotics and automation, neither of which require artificial intelligence. Now, recent AI innovations like ChatGPT seem poised to make white-collar jobs vulnerable, which were previously considered safe from automation. But will stop at white-collar positions, or will it threaten blue-collar jobs, as well? AI Blue-Collar Jobs Most at Risk From AI Most of the jobs generative AI can eliminate are white-collar roles. However, a few blue-collar fields could also be affected. Jobs that a robot can perform are at an especially high risk of being eliminated with AI integrations. Retail Sales and retail employees are already experiencing the impact of AI and automation in the job market. Over the past several years, more stores have replaced traditional checkout counters with automated self-service kiosks. Additionally, many people have shifted to online shopping, reducing the number of customers visiting physical storefronts. Self-service checkout at brick-and-mortar grocery stores. Some small shops have no employees, only a few associates who come in to restock the shelves. accounts for 55% of all transactions Unfortunately for people working in retail, most customer-facing roles will likely disappear in the near future. Restocking will likely become the only task requiring employees. AI could completely take over most checkout and customer service tasks. Manufacturing Automation has long been a part of the manufacturing industry, but AI could advance automation to the point of eliminating most hands-on jobs. Combining robotics and AI enables manufacturers to automate their assembly processes. Shifting to an employee-free workplace lets manufacturers operate around the clock and significantly reduce payroll, insurance, and injury compensation expenses. Plus, AI and robotics can often deliver higher-quality products since they don’t encounter human error. There are clear financial benefits for a fully automated manufacturing environment. AI will likely take over most assembly line jobs with the help of robotics. Most blue-collar positions in manufacturing will be technician roles, such as robot repair. Agriculture and Food Processing Farming is often not the first thing that comes to mind when people think of AI and robotics. However, there is huge potential for AI-assisted automation in the agriculture industry. Blue-collar jobs like food packaging and crop harvesting are often repetitive manual labor. These tasks are relatively easy to automate, especially now that AI can accurately recognize ripe or bruised fruit. For example, apple orchards in America’s Northwestern states are to pick apples. Some are flying drones that pick them from the air, while others are large robotic arms on a rolling base that drive through orchards. The robots use AI computer vision to identify ripe fruit and pluck apples from the trees with a robotic arm. starting to use AI-powered robots Farmers can completely automate their harvesting process with enough of these robots, eliminating thousands of blue-collar manual labor jobs. The same applies to food processing and packaging tasks, such as cleaning freshly harvested food. AI computer vision enables robots to perform virtually all food processing tasks, even with delicate foods like pastries and fruit. Is AI Replacing Employees or Filling Empty Jobs? It may sound alarming that AI is threatening blue-collar as well as white-collar jobs. However, is AI actually displacing human employees? It’s easy to imagine businesses firing people and replacing them with AI, but that may not be the reality of the situation. Many industries face major labor shortages today, including manufacturing, retail, and agriculture. This hurts productivity and leads to higher rates of stress and injuries among understaffed teams. These shortages are only getting worse, too. For instance, the National Association of Manufacturers estimates that the manufacturing industry by 2030. Additionally, studies show that fully in-person jobs, such as in retail, and lower employee satisfaction. Businesses have a hard time finding and keeping workers in these sectors. will be short 2.1 million employees have higher quit rates There is no lack of open blue-collar jobs. The problem is finding enough people to fill those roles. Employers often turn to AI and automation when labor shortages continue for an extended period. In these cases, integrating AI can improve the employee experience by reducing the workload on understaffed teams. Adapting to AI in the Workforce Is it possible for AI and employees to coexist peacefully in the blue-collar job market? Some may be surprised to hear that AI can be a helpful addition to the workforce in several ways. It may not be replacing employees so much as shifting humans’ jobs. One of the most common challenges in today’s workforce is a lack of skilled labor, specifically in blue-collar roles. Jobs are available, but not enough people have the technical skills to fill them. For example, there is a , which analysts expect to worsen over the next decade. shortage of at least 70,000 skilled HVAC technicians AI can help employers adapt blue-collar roles to close the skills gap. For instance, it can speed training processes to help employees gain the necessary skills in less time. This benefits everyone since more advanced abilities result in higher pay for blue-collar workers. Similarly, AI is creating more opportunities for blue-collar workers to move into higher-level jobs. AI is eliminating low-skill manual labor tasks, but that includes many positions people don’t typically enjoy anyway. The skills gap presents a great opportunity to reskill and upskill manual labor employees so they can graduate to more fulfilling, engaging careers. It’s also worth considering that AI is only good at a narrow niche of tasks. Humans will always : creativity, originality, and the ability to learn. Countless blue-collar jobs require those skills. have the edge in three core areas For instance, auto technicians need creativity to find the best possible solution to vehicles’ mechanical problems. Robotics technicians rely on their ability to learn to help them keep up with advancing technology. Landscapers exercise originality every day to create unique outdoor spaces. AI can’t replicate these skills on a human level. Even with the help of robotics, there will always be blue-collar jobs that require people. While AI automates mundane manual labor, humans get to focus on creativity-centered tasks. The physical nature of blue-collar work also makes it much more resilient to automation than most white-collar jobs. These careers will be more secure as AI’s presence in the workplace grows. Timeline for the Shift to AI in the Job Market How rapidly will AI change the blue-collar job market? It has advanced rapidly over the past few years, but its impact on the workplace is developing a bit slower. Employers need time and money to invest in AI tools and learn how to use them. Therefore, the transition will happen gradually. The first stage in most fields will be integrating AI tools and assistants. This is already occurring in many white-collar jobs. For example, software developers to automate many tasks, like idea generation or debugging. AI may start helping with employee training or managerial processes like shift scheduling in blue-collar fields. now use machine learning This first stage will occur over the next three to five years. Meanwhile, manufacturing specifically will see an increase in physical automation. For example, AI will begin taking over quality control inspections with the help of computer vision. Most of these integrations will fill empty jobs rather than replace employees. The shift to AI will take longer in some blue-collar fields than in others. For instance, manual labor in agriculture is highly threatened by AI, but the technology still needs much more development. As of 2023, agricultural employees are in the United States. among the least exposed to AI Agriculture has very little advanced electronic equipment, so adding AI will take longer than in fields like manufacturing. Engineers must develop outdoor-ready robotics equipment as well as AI models suited for agriculture. It could take another five to 10 years before tasks like harvesting are viable for automation. On the other end of the spectrum, retail jobs could begin seeing a decline due to AI within the next few years. Automation is already rapidly phasing out tasks like employee-serviced checkout lanes. The technology to automate retail is already present in the workplace, so these blue-collar jobs will experience the impact of AI soonest. Working Alongside AI Successfully It’s natural to be concerned about AI eliminating valuable jobs. Most blue-collar careers are at much lower risk of automation than white-collar jobs. However, AI poses a significant threat to manufacturing, retail, and agriculture careers. Luckily, it will mainly fill empty positions in these industries rather than displacing employees. AI can be a valuable assistant in many situations by reducing workloads and stress.

Is There an AI Threat to Blue-Collar Jobs?

HAMILTON K. FISKER AGAIN

THE STORM BURSTS.

One of the strong use cases for MinIO is the fact that it can run anywhere and on everything. As the industry slowly shifts towards repatriating data to a colo or data center, more and more companies want the same object storage capabilities that they had in the cloud, with full control of the infrastructure. Why would you want to have data closer to home? There are a number of reasons, but first and foremost is cost. The public cloud has become very expensive. For example, some time ago I had an ElasticSearch managed cluster running in AWS. I was eager to try out this new managed service, but I was not eager to discuss my surprise $30K bill with my boss. This was a painful, yet familiar wake-up call because at that moment I realized that I had just paid AWS six months of cloud budget to do something that I could have set up myself. The moral of the story is that unless you are very careful and monitor your cloud spend closely, it can get out of control very fast. There is also the matter of security. No matter where your data is located in the public cloud, it's almost always on a node or storage pool that is shared by someone completely unrelated to you; this is the nature of the cloud because it is how virtualization works. The cloud provides a warm feeling of comfort because now someone else must tackle security challenges, but if there are any security-related issues, there will be no insight into the issue (if someone was even able to detect it) and how to resolve it. The feeling of comfort rapidly evaporates when you get stuck securing someone else’s infrastructure in order to protect your data. Many enterprises have enjoyed the return to total control provided by repatriating to MinIO onto hardware they manage. To make the most of your repatriation efforts, MinIO comes with a number of enterprise-ready features such as to ensure data integrity, to siphon off data to a cold storage tier, which saves objects as a collection of data and parity blocks, and reconstructs them on the fly without any additional hardware or software. In addition to these, MinIO supports both at and in . This ensures that data is encrypted in all facets of the transaction from the moment the call is made until the object is placed in the bucket, where it is then protected with IAM S3-style policies and a built-in or external IDP, see for more information. Bitrot Protection Tiering Erasure Coding Encryption rest transit MinIO Best Practices - Security and Access Control Repatriation must be planned thoroughly and carefully. If you are dealing with petabytes of data generally it's more cost-effective to have your own infrastructure and servers to run on, you can even build a private cloud with your own (or leased) hardware. In addition, this also includes managing the real estate (colo space), power/UPS, cooling/HVAC, among other components. Don’t be deterred by these as we will demonstrate to you how you can migrate yet the overall ROI is still better than in the public cloud. A private cloud is like an apartment (as our CEO AB Periasamy likes to say). You are in full control of the costs and expenses that are associated with it, you never wake up to an alert of a surprise bill caused by some recursive loop function that ran overnight. There is, of course, some friction to moving when you are trying to make things better, for instance, when you are trying to expand a highway you inevitably have to close down some lanes so construction can proceed safely, but once it's done you will not only be able to drive on the original lanes but also the newly built ones to handle the capacity. Two of the most important cost considerations we need to make in the public cloud are the amount of storage space you need and egress costs when accessing/moving that data – these can be around 39% and 42% higher respectively in comparison to your own hardware in your data center or colocation facility. In addition to this, some of the other cost factors to consider are software, hardware, networking/switches, real estate/rack space/colocation rental, S3-API calls – everything you can think of and more. Learn more about possible savings that result from moving to your own private cloud in . The Lifecycle of the Cloud Between the public cloud and your data center exists a middle ground where you can have full control over infrastructure hardware, without the high initial cost of investment. , as the name states, provides bare metal servers with the exact specifications requested by the customer. If you want to use NVMe SSDs, then you can add those disks to the bare metal server. Equinix provides a management API to simplify hardware deployment and operations. To the developer/end user, it's as straightforward as launching an instance in the cloud. In fact, there is even a Terraform provider for Equinix Metal (which we’ll show you later). Equinix Metal Let's get started! Deploy the Infrastructure While we can deploy resources manually, the DevOps in me wants to automate at least some of the repetitive portions of this process to save time and effort,  especially when we want to do site-to-site replication among other things. Set Up Equinix Metal Terraform Equinix is one of the few bare metal providers that have an API to fully automate the infrastructure management process. Using their , you can automate deploying physical servers, shutting them down and even terminating them. You can do all this without using your own hardware, switches, routers and other resources. This is as close as you can get to public-cloud-level automation while still guaranteeing that no one else is sharing your hardware. Because Equinix Metal supports a myriad of instance types and storage options and interconnects such as SAS or SATA, and SSD, NVMe SSD, or HDD, in a variety of sizes. You can also configure the hardware that MinIO runs on to your exact specifications – right down to the exact type of drive to house MinIO partitions. API No one expects you to write Python scripts to talk to the Metal API; Equinix Metal has a Terraform Provider that allows us to connect to it and provide the high-level information needed to deploy cluster resources, while abstracting the internal jugglery required to get the networking, hardware, MinIO and other applications set up. provider "metal" {
auth_token = var.auth_token
} If you don’t have Terraform installed already, you can download it from their . downloads page Clone the GitHub repo to your local workstation. equinix/terraform-metal-distributed-minio git clone https://github.com/equinix/terraform-metal-distributed-minio.git Go into the repo and initialize Terraform so that it can download all the required modules and plugins from upstream. $ cd terraform-metal-distributed-minio
$ terraform init This will ensure all the modules required are downloaded automatically. Now, let's make sure a couple of mandatory variables are set. You can either set them as or there is a file in the repo cloned above called , which you can copy as . environment variables vars.template cp vars.template terraform.tfvars Ultimately, whichever method you choose, you need to set the following two variables auth_token project_id You can find more information on these in the docs. API There are several other variables that you can modify in , and we will modify the following later when we do site-to-site replication. terraform.tfvars facility: https://deploy.equinix.com/developers/docs/metal/locations/metros/ minio_region_name: The region where the MinIO cluster is deployed. Once you have your preferred configuration set, apply the Terraform plan. If the plan looks okay, run the command. approve $ terraform plan
$ terraform apply --auto-approve If the resources have been applied properly with the right configuration then the resulting output should look something like this Apply complete! Resources: 10 added, 0 changed, 0 destroyed.

Outputs:

minio_access_key = Xe245QheQ7Nwi20dxsuF
minio_access_secret = 9g4LKJlXqpe7Us4MIwTPluNyTUJv4A5T9xVwwcZh
minio_endpoints = [
  "minio-storage-node1 minio endpoint is http://147.75.65.29:9000",
  "minio-storage-node2 minio endpoint is http://147.75.39.227:9000",
  "minio-storage-node3 minio endpoint is http://147.75.66.53:9000",
  "minio-storage-node4 minio endpoint is http://147.75.194.101:9000",
]
minio_region_name = us-east-1 This is the whole shebang. When you see this output, not only have your physical servers been provisioned, but also MinIO has been deployed to these nodes, and the nodes have been configured as a cluster of distributed storage. Access MinIO Cluster We used Terraform to automate most of the process, so now all that is left is to access the MinIO cluster. Our recommended tool is to use . Use the following command to download the binary mc curl https://dl.min.io/client/mc/release/linux-amd64/mc \
  --create-dirs \
  -o $HOME/minio-binaries/mc

chmod +x $HOME/minio-binaries/mc
export PATH=$PATH:$HOME/minio-binaries/ Create an alias that points to the MinIO cluster we deployed mc config host add minio1 $MINIO_ENDPOINT $MINIO_ACCESS_KEY $MINIO_SECRET_KEY You can replace the variables above with the values that you set while launching the MinIO cluster via Terraform, but make sure to set the alias name to . This will make sense later when we show you how to do site-to-site replication. minio1 Check to see if you are able to connect successfully by fetching some metadata from the cluster $ mc admin info minio1 --json | jq .info.backend

{
  "backendType": "Erasure",
  "onlineDisks": 48,
  "rrSCData": 6,
  "rrSCParity": 2,
  "standardSCData": 6,
  "standardSCParity": 2
} If you see an output similar to the above then you are able to successfully access the MinIO cluster via the command. So what’s next? When should we migrate the data from S3? mc Load Balancing the MinIO Cluster We can migrate the data from S3, or even add some of our own data, and start using the cluster. But let’s take it one step further. We want to achieve the same level of redundancy as AWS S3, meaning if one site goes down we want to make sure our data is accessible on another site. AWS accomplished this with regions, but how do we accomplish this with MinIO? Now, we can see the beauty of the little automation we did with Terraform earlier. Let me show you how simple it is to get another MinIO region up in Equinix Metal. Lets our source repo again, but this time in a new directory git clone terraform-metal-distributed-minio-site-2 git clone https://github.com/equinix/terraform-metal-distributed-minio.git terraform-metal-distributed-minio-site-2 Go into the repo and initialize Terraform so that it can download all the required modules and plugins from upstream similar to the original MinIO deployment. terraform-metal-distributed-minio-site-2 $ cd terraform-metal-distributed-minio-site-2
$ terraform init Once all the modules have been downloaded, copy the variables file and set the two variables cp vars.template terraform.tfvars auth_token project_id So far the process should look very similar to how we launched the first cluster, but this is where things will differ. Let's set the variables that differentiate the second site from the first site. First, let's set the to or pick one from this list of . Next set the to or anything that differentiates it from the other cluster. facility sv16 facilities minio_region_name us-west-1 Run the plan to ensure the changes you made get reflected in the output. $ terraform plan
$ terraform apply --auto-approve If the resources have been applied properly, with the right configuration, then the resulting output should look something like this Apply complete! Resources: 10 added, 0 changed, 0 destroyed.

Outputs:

minio_access_key = Xe245QheQ7Nwi20dxsuF
minio_access_secret = 9g4LKJlXqpe7Us4MIwTPluNyTUJv4A5T9xVwwcZh
minio_endpoints = [
  "minio-storage-node1 minio endpoint is http://144.45.65.29:9000",
  "minio-storage-node2 minio endpoint is http://144.45.39.227:9000",
  "minio-storage-node3 minio endpoint is http://144.45.66.53:9000",
  "minio-storage-node4 minio endpoint is http://144.45.194.101:9000",
]
minio_region_name = us-west-1 If you see as then you have successfully launched the second cluster. Let's get that added to . minio_region_name us-west-1 mc mc config host add minio2 $MINIO_ENDPOINT $MINIO_ACCESS_KEY $MINIO_SECRET_KEY Set the alias name to and check to see if you are able to connect successfully by fetching some metadata from the cluster minio2 $ mc admin info minio2 --json | jq .info.backend

{
  "backendType": "Erasure",
  "onlineDisks": 48,
  "rrSCData": 6,
  "rrSCParity": 2,
  "standardSCData": 6,
  "standardSCParity": 2
} At this point, you should have 2 sites: and . minio1 minio2 Let's set up replication across both clusters $ mc admin replicate add minio1 minio2
Requested sites were configured for replication successfully. Verify the both sites are configured correctly mc admin replicate info minio1

SiteReplication enabled for:

Deployment ID                    	| Site Name   	| Endpoint
f96a6675-ddc3-4c6e-907d-edccd9eae7a4 | minio1      	| http://<site1_public_ip>
0dfce53f-e85b-48d0-91de-4d7564d5456f | minio2      	| http://<site2_public_ip> Check to make sure replication is working properly mc admin replicate status minio1

Bucket replication status:
No Buckets present

Policy replication status:
●  5/5 Policies in sync

User replication status:
No Users present

Group replication status:
No Groups present Test by creating a bucket in minio1 /opt/minio-binaries/mc mb minio1/testbucket Add any object into the bucket /opt/minio-binaries/mc cp my_object  minio1/testbucket List the objects in the other sites, in this case on minio2 /opt/minio-binaries/mc ls minio2/testbucket
[2023-07-20 18:52:09 UTC] 3.0KiB STANDARD my_object As you can see it's almost instantaneous to replicate data to other MinIO deployments, even though they are geographically disparate. Let’s do a quick test to see if this is really as simple as it appears. Remember that MinIO is a drop-in replacement for AWS S3, so everything that is supposed to work with S3 will work with MinIO too. In this case, we will use a Terraform to upload an object to a MinIO bucket. In Terraform this is done through the AWS provider which is essentially a module that connects to the AWS API to perform various operations in the AWS ecosystem, but in this case, we will use the Terraform AWS S3 resource to access MinIO bucket. Create an AWS provider in Terraform like below. Make sure you update the details to match the Equinix Metal cluster we just deployed. minio1 provider "aws" {
    region = "us-east-1"
    access_key = "Xe245QheQ7Nwi20dxsuF"
    secret_key = "9g4LKJlXqpe7Us4MIwTPluNyTUJv4A5T9xVwwcZh"
    skip_credentials_validation = true
    skip_metadata_api_check = true
    skip_requesting_account_id = true
    s3_force_path_style = true
    endpoints {
        s3 = "http://147.75.65.29:9000"
    }   
} Upload a file using the terraform resource aws_s3_bucket_object resource "aws_s3_bucket_object" "object" {
    bucket = "public"
    key = "my_file_name.txt"
    source = "path/to/my_file_name.txt"
    etag = filemd5("path/to/my_file_name.txt")
} As you can see above, we haven’t used any MinIO-specific Terraform resource, we are using AWS provider resource. Even though we are using the existing AWS S3 Terraform resource, the object store is completely powered by production enterprise-grade MinIO. aws_s3_bucket_object Migrating Data from AWS S3 We now have all the building blocks ready to go for you to have production-grade object storage and total control of the entire infrastructure. Next, we’ll migrate data that’s already in S3. There are a number of ways you can migrate your data from AWS S3 to MinIO, but the one we recommend is using . mc is a Swiss army knife of data synchronization. It can copy objects from S3 or S3-API-compatible object stores and mirror them to MinIO. One of the more popular use cases of this is mirroring an Amazon S3 bucket to MinIO in order to expose data to non-AWS applications and services. mc mirror Create a new with an access key and secret key, allowing access only to our bucket. Save the generated credentials for the next step. IAM policy /opt/minio-binaries/mc alias set s3 https://s3.amazonaws.com BKIKJAA5BMMU2RHO6IBB V7f1CwQqAcwo80UEIJEjc5gVQUSSx5ohQ9GSrr12 --api S3v4 Use to copy the data from S3 to MinIO mc mirror mc mirror s3/mybucket minio1/testbucket Depending on the amount of data, network speeds and the physical distance from the region where the bucket data is stored, it might take a few minutes or more for you to mirror all the data. You will see a message when is done copying all the objects. mc Once the data is copied or while the data is being copied, list the contents of the bucket in site. You will notice that some of the data is already there from . minio2 minio1 /opt/minio-binaries/mc ls minio2/testbucket
[2022-12-19 18:52:09 UTC] 3.0KiB STANDARD all_object s Ultimately the laptop where you are running from is a bottleneck because the data has to traverse through the system where command is executed. This could be several petabytes of data which could take days if not weeks to migrate depending on the network speed. To migrate data from S3 to MinIO, there is a more efficient method called Batch Replication, please see to learn more about Batch Replication and other migration best practices. mc mirror mc mirror How to Repatriate from AWS S3 to MinIO Put the Pedal to the Metal This blog post demonstrated that MinIO running on Equinix Metal NVMe SSDs, in a site-to-site replication configuration, will give you the same level, if not more, of performance, data durability and resilience at a fraction of the cost of S3 while retaining full control over your cloud. Do you have 100% control of all the infrastructure? Not quite. The switches, routers and other networking gear are managed by Equinix, but the advantages of being on their network exceed the disadvantages. You can get point-to-point, WAN, or other dedicated circuits to virtually connect to any other provider out there. In essence, you can have a private circuit connected directly to AWS (via Equinix Connect) and then you can move your data 10 times as fast, all the while being secure because it's not traversing the open public internet, and only your data goes through that circuit. Moreover, MinIO repeatedly show very little (<1%) throughput performance degradation with encryption turned on, therefore we recommend that all MinIO deployments use encryption at rest and all MinIO deployments should also using TLS. Congratulations, now your data is on a more secure,  yet transparent, system where you have full control and accountability. benchmarks secure network communications If you have any questions on migrating from AWS S3 to MinIO on Equinix Metal, be sure to reach out to us on ! Slack Also published . here

How to Migrate from AWS S3 to MinIO on Equinix Metal

ইকুইনিক্স মেটালে কীভাবে AWS S3 থেকে MinIO-তে স্থানান্তর করবেন

So migrieren Sie von AWS S3 zu MinIO auf Equinix Metal

Cómo migrar de AWS S3 a MinIO en Equinix Metal

Comment migrer d'AWS S3 vers MinIO sur Equinix Metal

इक्विनिक्स मेटल पर AWS S3 से MiniIO में कैसे माइग्रेट करें

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