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How to Use Swift for Web Development

ওয়েব ডেভেলপমেন্টের জন্য কীভাবে সুইফট ব্যবহার করবেন

So verwenden Sie Swift für die Webentwicklung

Cómo usar Swift para el desarrollo web

Comment utiliser Swift pour le développement Web

वेब डेवलपमेंट के लिए स्विफ्ट का उपयोग कैसे करें

Swift を Web 開発に使用する方法

웹 개발에 Swift를 사용하는 방법

Como usar o Swift para desenvolvimento web

Web Geliştirme için Swift Nasıl Kullanılır?

Cách sử dụng Swift để phát triển web

如何使用 Swift 进行 Web 开发

This is the 11th post in my Junior Developer Diaries blog series. I’m writing more every week, and you can sign up to hear more and read previous posts on my website. As a software developer, having the ability to learn and adapt is probably the most crucial skill to have. Coming at a problem with no prior knowledge, sitting down and figuring it out is what you’re paid to do. But how do you get better at learning? And how do you make sure that time isn’t wasted? It’s All About You A usual bit of Junior Dev Diaries advice is introspection and self-awareness. Take a good look inside yourself and figure out what works for you on learning. If you’re reading this, you’ve probably already done some some high school, maybe even some tertiary education such as college, university, tech/tafe or a bootcamp. By now you should know what works and doesn’t work for you. If you haven’t, consider this — what do you know the best right now? What do you know the least? (Choose things you feel you put equal effort into). Now, try what you did while you were learning that? Did you stick to videos? Written tutorials? A fun project? Were you at work? Side project? Did you stand to get some sort of financial gain? reverse engineer Taking a look at these factors can tell you a lot about how your own brain works, and then you and your brain can work together on the next piece of new tech that comes along. It’s Meant To Be Learnable A lot of the tech you’ll be learning probably is designed to be learnable. It’ll have docs, tutorials, example code, etc. Someone has spent their time writing that with the hope that you’ll understand it. If you’re struggling, just keep this in mind and try again. Also, if you find the documentation lacking, you can always fix that with a pull request. Also do a search on YouTube, for every technology there’s at least one video tutorial out there for it. Thank you, YouTubers! Mental Models One of the more common traps when learning new stuff is going into a new technology with a faulty mental model of how something should work. Your brain starts to skim the content looking for matches to expected components of the technology, and they just don’t ever show up, and you get no where. If that’s the case, stop, breath, and drop all your assumptions and try again. This may well be completely new to you — and really, a little bit of knowledge can sometimes be worse than none at all. Document Your Learning When you’re learning a lot or doing a bunch of research all at once. Start a running doc (or something) and copy in urls, terminal commands, observations etc. It’s really easy to get lost when you’re hacking together something so it’s good to have your history noted down. And, once you’ve done that, you can document (not create) your learning as a blog post, as . Once you have this, others can see the path you took and software developers find these experiences valuable for improving developer experience and documentation. per my earlier post Augment Your Learning Augment your learning by attending meetups. Get out there and socialise with others learning about the same tech, and hear from thought leaders and experts in the topic. Rightly or wrongly, a lot of people in this industry love to explain tech to newbies, so it’s of great advantage to you to listen and learn! In addition to meetups, there are often and to subscribe to on the topic. Not to mention Twitter and it’s use of hashtags, such as #swiftlang or #golang. Slack Groups tech newsletters Share What You Learn Another theme readers will know from me is paying it forward, and sharing what you’ve learned is a great way to do that. (Heck, it’s what I’m doing right now!). For new things learned, my personal favourite is presenting in the office at our weekly “Tech Awesome”, which is a chance to share what you’ve learned. A simple blog post with a decent dash of humility thrown in also works well. Don’t try to act too authoritative on the topic, you’re just new. Choose What To Learn Carefully Finally, don’t spread yourself too thin. There’s no way you can learn everything but a little bit of a lot of things will be helpful. Try to find something you like (or 2–3 things) and go deep on those — this will be what you introduce yourself as (for example, I introduce myself as a ruby and iOS developer… but am familiar with EmberJS, Elixir, Python, and even some Go and Scala and DevOps techniques). Pick what you want to learn and go with that. I’ve written more about this in one of about choosing a direction as a junior developer. my earlier posts As you set out to improve your pace and skills in learning, I wish you the best of luck. Its about knowing yourself, getting stuck in and understanding it well enough to teach another and bring your team forward. Have fun! This is the 11th post in my Junior Developer Diaries blog series. I’m writing more every week, and you can sign up to hear more and read previous posts on my website.

How to Supercharge your Learning of New Technologies

SwiftLint is the best tool to enforce coding standards and best practices. It comes with pre-defined rules that can be customized to suit individual project needs. However, sometimes the built-in rules fall short of project requirements. In such cases, we write regular expressions to fill the gap. While regex is a powerful tool, creating complex rules can be difficult or even impossible. This is where native SwiftLint rules come in. They're just like the rules that come with SwiftLint - they're written in Swift, efficient, and can catch more violations compared to regex. https://twitter.com/simjp/status/1558168307756318720?s=20&embedable=true The final project can be found at https://github.com/jpsim/swiftlint-bazel-example Setting Up the Project Install Bazel if not installed. brew list --formula | grep -q "bazel" || brew install bazel Start by creating a new directory. mkdir MySwiftLintRules && cd MySwiftLintRules Next, set up a new Bazel workspace. This involves creating several files and enabling bzlmod: touch .bazelrc WORKSPACE MODULE.bazel BUILD && echo "common --enable_bzlmod" > .bazelrc Add the boilerplate code to the MODULE.bazel module(
    name = "swiftlint-bazel-example",
    version = "0.0.0",
    compatibility_level = 1
)

bazel_dep(name = "swiftlint", version = "0.51.0", repo_name = "SwiftLint")
extra_rules = use_extension("@SwiftLint//bazel:extensions.bzl", "extra_rules")
extra_rules.setup(srcs = "@swiftlint-bazel-example//swiftlint_extra_rules:extra_rules")

bazel_dep(name = "rules_xcodeproj", version = "1.4.0") Add the boilerplate code to the file. This will add Xcode support for our Bazel project. BUILD load("@rules_xcodeproj//xcodeproj:defs.bzl", "xcodeproj")

xcodeproj(
    name = "swiftlint_xcodeproj",
    project_name = "SwiftLint",
    tags = ["manual"],
    top_level_targets = [
        "@SwiftLint//:swiftlint",
        "@SwiftLint//Tests:ExtraRulesTests",
    ],
) Create a directory to store the source code for your rules. mkdir swiftlint_extra_rules && touch swiftlint_extra_rules/Rules.swift && touch swiftlint_extra_rules/BUILD And for the last time, paste the boilerplate code to the file swiftlint_extra_rules/BUILD filegroup(
    name = "extra_rules",
    srcs = glob(["**/*.swift"]),
    visibility = ["//visibility:public"],
) At this point, you should be able to build SwiftLint using the command. bazel build Developing Rules With Xcode We’ve added dependency to our project to make developing your custom rules easier. So, let’s generate our project: rules_xcodeproj bazel run swiftlint_xcodeproj && open SwiftLint.xcodeproj -a Xcode Now, we can build and debug the scheme. You can use this app as usual with swiftlint binary installed from the homebrew. swiftlint Try running it on your project: Click the scheme name in the , click “Edit Scheme…“, and set the "Working Directory" in the "Options" tab to the path where you would like to execute SwiftLint. toolbar of your project window For more information on developing native SwiftLint rules, check out SwiftLint’s CONTRIBUTING.md Adding a Custom Rule In this example, we are creating the "forbidden var rule," as shown in the video tutorial at . https://vimeo.com/819268038 Copy and paste this code into the file, then run the scheme in Xcode. Rules.swift swiftlint import SwiftSyntax

// This function should return an array containing all of your custom rules
func extraRules() -> [Rule.Type] {
	[ForbiddenVarRule.self]
}

struct ForbiddenVarRule: ConfigurationProviderRule, SwiftSyntaxRule {
	var configuration = SeverityConfiguration(.error)

	init() {}

	static let description = RuleDescription(
		identifier: "forbidden_var",
		name: "Forbidden Var",
		description: "Variables should not be called 'forbidden'",
		kind: .idiomatic,
		nonTriggeringExamples: [
			Example("let notForbidden = 0")
		],
		triggeringExamples: [
			Example("let ↓forbidden = 0")
		]
	)

	func makeVisitor(file: SwiftLintFile) -> ViolationsSyntaxVisitor {
		Visitor(viewMode: .sourceAccurate)
	}
}

private extension ForbiddenVarRule {
	final class Visitor: ViolationsSyntaxVisitor {
		override func visitPost(_ node: IdentifierPatternSyntax) {
			if node.identifier.text == "forbidden" {
				violations.append(node.identifier.positionAfterSkippingLeadingTrivia)
			}
		}
	}
} And that's it! You've created a custom SwiftLint rule and integrated it into your Swift project using Bazel. With this setup, you can create and test as many custom rules as you need, helping to ensure that your code stays consistent and conforms to your team's coding standards. Happy linting!

Embrace the Power of Custom SwiftLint Rules and Say Goodbye to Regex

কাস্টম সুইফ্টলিন্ট নিয়মের ক্ষমতা আলিঙ্গন করুন এবং Regex কে বিদায় বলুন

Nutzen Sie die Leistungsfähigkeit benutzerdefinierter SwiftLint-Regeln und verabschieden Sie sich von Regex

Adopte el poder de las reglas personalizadas de SwiftLint y diga adiós a Regex

Adoptez la puissance des règles SwiftLint personnalisées et dites adieu à Regex

कस्टम स्विफ्टलिंट नियमों की शक्ति को अपनाएं और रेगेक्स को अलविदा कहें

カスタム SwiftLint ルールのパワーを活用し、正規表現に別れを告げましょう

사용자 정의 SwiftLint 규칙의 강력한 기능을 활용하고 Regex에 작별을 고하세요.

Abrace o poder das regras personalizadas do SwiftLint e diga adeus ao Regex

Özel SwiftLint Kurallarının Gücünü Kucaklayın ve Regex'e Elveda Deyin

Nắm bắt sức mạnh của các quy tắc SwiftLint tùy chỉnh và nói lời tạm biệt với Regex

拥抱自定义 SwiftLint 规则的力量并告别正则表达式

The SaaS market keeps soaring rapidly, and global is expected to reach $232 billion by the end of 2024, a testament to its widespread adoption across industries. But within SaaS, there is a field-specific approach - vertical SaaS, a customized fit that addresses the individual demands and challenges of concrete sectors. SaaS revenue Vertical SaaS emerges as a beacon of specialization and customization as the continues its meteoric rise. While horizontal SaaS solutions provide broad functionality applicable across various sectors, Vertical SaaS takes a surgical approach, crafting bespoke applications finely tuned to the demands of markets. SaaS business model This tailored strategy not only ensures alignment with sectoral workflows and regulations but also fosters deeper engagement and loyalty among users. In an era where personalization reigns supreme, Vertical SaaS exemplifies the power of niche focus within the expansive , driving innovation and value creation in specialized domains. SaaS ecosystem Vertical SaaS Benefits As industries become more specialized and competitive, the need for customized software solutions is growing. In this setting, Vertical SaaS is a valuable asset, offering targeted features and unmatched advantages: Field-Specific Functionality: It is focused on selected fields and meets their requirements. It streamlines workflows, manages teamwork, and solves problematic issues faced by. Cost Savings Opportunities: While SaaS may have a higher upfront cost due to its specialization, it provides long-term savings; Tailored decisions reduce unnecessary features and expenses. Reduced Competition: This operates in a niche, facing less direct competition. This allows providers to focus on quality and industry expertise. Low Customer Churn: Custom solutions lead to higher customer satisfaction and retention. Customers appreciate tailored features that align with their unique requirements. High Growth Potential: With the development of the technology landscape, Vertical SaaS can adapt and expand within its niche. The potential for growth remains significant. Increased Customer Trust: Field-focused solutions build trust with customers. Companies value suppliers who understand their business needs. Difference From Horizontal SaaS Model Vertical SaaS and horizontal SaaS represent divergent strategies in software delivery, tailored to meet distinct market demands. Horizontal SaaS provides versatile solutions with broad industry applicability, fostering adaptability and scalability. Conversely, Vertical SaaS focuses on finely tuned offerings crafted for niche markets, tackling specialized challenges with exactitude. This comprehension is pivotal for businesses aiming to maximize the efficacy of software investments and harmonize them with strategic goals. Delve deeper into the unique traits of these methodologies in the subsequent analysis: Industry Focus: Vertical SaaS is­ highly specialized to mee­t the unique require­ments, regulations, and workflows of particular sectors, such as he­althcare, finance, or real e­state. In contrast, horizontal SaaS offers broad functionalities that are­ applicable across diverse fields. Functionality: Ve­rtical SaaS platforms are designed with a spe­cific area focus. They streamline proce­sses, manage workflows, and tackle spe­cialized missions within a particular vertical. On the othe­r hand, horizontal SaaS solutions offer a more extensive spectrum of ge­neral business functions that can be used and use­d across various sectors. Customization: Vertical SaaS are­ easy to configure, and tailored to the unique­ needs of the spe­cific field they serve. They provide targeted solutions that close­ly align with the industry's requireme­nts. Horizontal SaaS, on the other hand, gene­rally offers more standardized fe­atures and configurations that cater to a broader audie­nce. While they may have­ some customization options, the leve­l of customization is typically lower compared to Vertical SaaS. Compe­tition: Vertical SaaS operates within a niche­ market, facing less direct compe­tition due to its specialized nature­. This allows providers to focus on delive­ring quality and establishing themselve­s as industry experts. Horizontal SaaS faces more­ competition in the broader marke­t, with numerous providers offering similar ge­neral-purpose solutions. This competition can be­ intense, leading to price­ wars and commoditization of services. Customer Base­: Vertical SaaS attracts customers in these sectors who value targeted decisions that match their goals. Horizontal SaaS appeals to a diverse array of businesses across industries, attracting customers looking for versatile tools that can be applied to different aspects of their operations. Examples of Vertical SaaS Companies Riskalyze (Nitrogen Platform) Financial Services Industry: Riskalyze provides a Vertical SaaS solution that helps financial advisors: Product: Manage 401(k) plans; Analyze investment risks; Create investment portfolios. Notable Feature: Their alignment tool allows advisors to measure risk capacity and tolerance, as well as portfolio risk on a scale from 1 to 991. It empowers firms to transform fearful investors who make bad decisions into fearless investors who make great decisions. These great decisions fuel advisory firms to deliver amazing long-term financial outcomes, turning prospects into lifelong clients. Applied Epic Insurance Industry: Applied Epic is a cloud-based software designed for independent insurance agencies. It offers a comprehensive suite of features to streamline agency operations and enhance efficiency. Product: Notable Feature: Applied Epic provides automated workflows that allow agencies to manage various aspects of their business seamlessly. These workflows cover: Property management; Sales tracking; Revenue management; Customer service deliveries; Integrated group texting; Data control. Users appreciate Applied Epic’s user-friendly dashboard that simplifies navigation and access to critical information. The software’s intuitive design ensures that insurance professionals can efficiently handle day-to-day tasks. Applied Epic empowers insurance agencies to operate easily, manage properties, track sales, and handle revenue—all within a single platform. ClearCare Industry: Health and Care. Product: ClearCare, based in the United States, provides a health and care solution. They offer: Electronic medical care records; 100% automatic labs for ease and swiftness. ClearCare's client relationship management software assists home care agencies by streamlining electronic health care records, including e-charts, e-prescriptions, and automated lab processes. By offering field-specific functionality, ClearCare exemplifies the power of vertical SaaS in meeting the distinct demands of healthcare providers.

What Is a Vertical Saas?

This is the first entry in my 100 days of challenge. I will share the things that I learn, related to Golang or its configuration in my environment, each day for 100 days. In this post, we will discuss the basics of Go Lang and why you should learn it. It's not an in-depth guide but a high-level overview for getting started with Go Lang. Go Lang A bit of Context In April 2021, I started learning and to date, I have more than 10 projects(not all deployed) created and publicly open on . It has been a great experience so far and I am still in between an internship with a project to be created in Django. The learning process after I started blogging in June 2021 has been a different fantasy, I have been enjoying the process of writing the things I learn. So, this Spring/Summer I am creating a challenge of 100 days for learning Go Lang and documenting the process. Hopefully, this will be a useful place to learn for a beginner and more than that a place for me to reflect on in the future. Django GitHub Leaving the rant aside, let's start with the best question to answer, What is Golang? What is Golang? (officially called Go) is a programming language developed by Google. It is statically typed like C\C++, Java, Kotlin, etc, with a syntax similar to dynamic languages like Python, Js, Ruby, Lua, etc. So, Golang provides the best of both worlds, the swiftness to write code and also providing control and memory safety. Golang Go is regarded as the language of the cloud i.e. it is heavily used in the cloud infrastructure, it involves creating microservices, APIs, containerization, orchestration, and a lot of other technologies. It can be a super useful programming language for DevOps folks and people into Cloud computing. Although Go is designed for Cloud Infrastructure, that doesn't mean you can't use it elsewhere. There are a lot of fields in which Golang nicely fits. We'll discuss why you would want to learn Golang and state the domains where it can be used. Go programmers are called Gophers Why learn Golang? There are a lot of reasons for learning Go as your next programming language. Not only it is quite famous but it is significantly faster than other programming languages like Python and Javascript. It is a simple language to pick up as it is designed for modern world technologies. There is an added advantage to learning Go Lang as it is a new programming language(not as old as Python). It was open-sourced in 2012 and has gotten a great boost in the last five years as cloud companies are on the rise. 1. GO lang is simple GO lang is a simple programming language. It is quite similar to in terms of its syntax, although it also inherits characteristics from the Java/C++ paradigm. It has a single entry point program via the function similar to Java/C++ languages and is compiled into binary/executable code before running. It has the standard library that provides a lot of functionalities out of the box. It provides some quite handy tools and has support for creating web applications in the core language itself. Python main 2. GO lang is fast (Execution Speed) Though it might not be the fastest language of all time, it beats Python and Javascript significantly in the context of backend development. Go lang is a compiled language and hence it has no overhead of converting into byte code or the interpreter. Go lang also checks for unused variables, functions, identifiers, and packages, this can boost the speed of execution by a good amount additionally optimizing the memory footprint of the program. We can see, that Go lang is almost equivalent to C and C++ programming languages, though this might not be the best comparison of speed as it depends on quite a lot of factors and the context of programs. Still, Go lang has a significant edge over Java and C++ which is a great factor because of the writing complexity involved in those programming languages. 3. GO lang is fast (Writing Speed) GO language is fast, yes I get that. It is faster in terms of writing speed. If you sit down to write the same code in Java/C++ or Go, having time constraints, you will move out with Go as a winner. It is much easier to write code in Go than compared to C/C++, Java, or even Javascript. Golang prefers clear code over fast code, though it might not be as fast as Rust or C++, it is definitely much easier and quicker to write than the other counterparts. Go is designed for writing software that can scale with large codebases and teams. So it's not a surprise it is used to create cloud computing technologies like Docker, Kubernetes, and other tons of cloud-native tools. 4. GO lang is made for the modern world Go lang is the modern C Go lang is a modern language, it is not as ancient as C/C++ or Java. It is designed for Cloud-native applications which are scalable and optimized for performance meeting the needs of the modern demand for bleeding-edge technologies. It is designed by the people who created the UNIX operation, Ken Thompson. Though there were other go authors like Robert Griesemer and Rob Pike. Go lang has been designed keeping the idea of C but also considering the context of the modern world. It makes tasks like concurrency a lot simpler but it avoids dangerous and manual control of memory by not allowing pointer arithmetic. Where you can use Golang? Go land finds its use case in a lot of domains including Cloud computing, Web applications, CLI and DevOps tools, and so on. As GO is a general-purpose programming language, you can use it anywhere in the relevant fields of technology. Cloud-native applications As Golang has capabilities to execute low-level system calls efficiently, cloud-native tools can be created in Golang to improve efficiency and memory usage. As Go uses fewer resources like CPU and memory, it can optimally be used for applications that require almost no delay in the response. It also handles concurrency very well using goroutines which are way simpler to implement. All these factors contribute very well to constructing a modern cloud-native application. Golang is used in Cloud-native technologies like: - containerization technology Docker - Container Orchestration Kubernetes - Cloud Hosting Platform Google Cloud This makes Golang widely adopted and has firm grounds for the future as well. Web applications As Golang is highly efficient and fast it can be used to develop web applications. It can scale a web application very well as it is a statically typed ecosystem. It also has extensive support for integrating databases into web applications. Golang doesn't require any 3rd party integrations to create the web apps, it is developed using internal or external packages and modules. The performance of Golang is the core feature for creating web apps in it, it heavily outperforms Javascript(Node) and Python. As golang code is compiled before running, this makes a single executable depending on the operating system, this improves the performance to a great extent. So, golang should be an ideal choice for developing web applications. - Music System Webapp Soundcloud - Cab Booking/Ride Sharing App Uber - E-commerce Webappp Allergo DevOps tools A lot of DevOps tools like Prometheus, Grafana, Terraform, and Hugo are written natively in Go. Having appropriate features like simplicity, scalability, efficiency, and concurrency, Golang fits the DevOps space really well. Golang is already used in cloud-native technologies as mentioned earlier adopting it in the DevOps space is a great choice as it creates an even and supportive ecosystem. The paradigm of Java is surprisingly built-in golang as well without conversion into byte code. This is a great feature any DevOps engineer would dream of, using a single code base for multiple platforms can save a lot of time and improve productivity. Write once and Run anywhere - Monitoring system and time series database Prometheus - Platform for monitoring and observability Grafana - Building, Changing, and Versioning Infrastructure Terraform - Run Kubernetes locally Minikube - standard library for microservices Kit Who uses Golang? Golang is a widely used programming language, it is used by some of the big companies around the world. Of course, Google uses it, apart from Google, it is being used by companies like: Netflix PayPal Microsoft Dropbox Meta (facebook) Uber Twitter Twitch There are so many tools created tools using golang like the gh-cli, cockrach-db, jwt, etc. You can see a whole list of applications/tools/frameworks written in Go from this awesome list Conclusion Go lang is an excellent choice for anyone into web development, cloud-native, DevOps, and CLI tools. Being a recently developed language, Go has grown a lot faster than any other language in the programming world. After learning Python over two years, I might want to dabble into Go but not completely leave Python. Python has a great community and is widely adopted in almost all fields of development. Still, trying out new things is what keeps you in the hunt for opportunities and meeting new people. So starting from today, I will share my learning a new thing in Go lang every day for 100 days. At least that's the plan. The post might be in the form of a long post or a short TIL on the blog. Check out the GitHub repository for the 100-day challenge . If you have any queries, suggestions, or feedback please let me know in the comments or on the mentioned social handles. Thank you for reading.  Happy Coding :) here Also published . here

Kickstarting Your GoLang Journey

The following is a tale that unfolds within the "Metamathematical" realm, where the boundaries between mathematics and narrative are blurred, giving rise to a unique realm of creation, understanding, and growth. It is a story that reminds us of the power of unity, adaptability, and transcendence, demonstrating that sometimes the most profound insights can emerge from the spaces between what we think we know and the unexpected connections we make along the way. This story was made with the help of Pi AI. Emma and the realm of Numberland Once upon a time, in the mystical realm of Numberland, there were two families who had lived in harmony for centuries: the Prompts and the Not-so-Prompts. The Prompts were known for their swiftness and efficiency, while the Not-so-Prompts were renowned for their intricate and challenging endeavors. Both families played vital roles in the prosperity and balance of Numberland, but an age-old question persisted: Were the Prompts and Not-so-Prompts fundamentally different, or were they simply two sides of the same numerical coin? The Prompts were led by the wise and just Queen Poly, who was known for her ability to solve problems with grace and speed. The Not-so-Prompts were guided by the enigmatic King Nilo, whose strength lay in tackling complex problems that seemed impossible to solve. For generations, the inhabitants of Numberland had pondered the relationship between the Prompts and Not-so-Prompts. Some argued that every Not-so-Prompt could, in essence, become a Prompt if only they could discover the right approach or "shortcut." Others insisted that there were inherent differences between the two families, and no amount of searching would uncover a universal key to transform one into the other. As the debate raged on, a young mathematician named Emma ventured into the heart of Numberland, determined to uncover the truth. Emma was fascinated by the dynamic between the Prompts and Not-so-Prompts and believed that the answer lay within the very fabric of their mathematical realm. Emma embarked on a quest to explore the diverse landscapes of Numberland, from the one-dimensional simplicity of Lineville to the multi-dimensional complexity of Fractalia. She studied the unique properties of each realm and observed how the Prompts and Not-so-Prompts interacted within them. In Lineville, she discovered that the Prompts and Not-so-Prompts were nearly indistinguishable, their differences blurred by the linear nature of the realm. However, in Fractalia, the distinctions between the two families were stark, and it seemed impossible to transform one into the other. As Emma's journey unfolded, she began to realize that the answer to the age-old question was not a simple matter of equality or inequality. Instead, she saw that the relationship between the Prompts and Not-so-Prompts was deeply intertwined with the mathematical landscapes they inhabited. Emma's discoveries ignited a revolution in Numberland. The inhabitants began to see the Prompts and Not-so-Prompts not as separate entities but as two sides of a larger, interconnected whole. They recognized that the true power of their realm lay not in the individual families but in the rich and diverse interactions that arose when Prompts and Not-so-Prompts worked together. In the end, Emma's quest did not lead to a definitive answer to the question of P vs. NP. Instead, it revealed a deeper understanding of the complexities and beauty of Numberland, showing that the realm's true potential lay in the harmonious dance between simplicity and complexity, swiftness and depth. And so, the inhabitants of Numberland learned to appreciate their differences and embrace the richness of their mathematical tapestry. As Emma's exploration of Numberland and Fractalia continued, her relentless pursuit of understanding the true nature of Prompts and Not-so-Prompts brought chaos to the once-harmonious realms. In response, the Prompts and Not-so-Prompts set aside their differences and united against a common threat for the first time in history. Their collective effort to protect their world resulted in the birth of three extraordinary children: Complexus, Spatius, and Timos, each representing a vital aspect of the intricate balance between the Prompts and Not-so-Prompts. Complexus, the embodiment of the interaction between Prompts and Not-so-Prompts, grew up to reveal their unique potentialities. With a deep understanding of the strengths and weaknesses of both families, Complexus guided the interaction landscape, determining where Prompts could flourish and where Not-so-Prompts could collaborate with Prompts to enhance their lives. In this way, Complexus ensured that the delicate balance between simplicity and complexity was maintained, allowing both families to thrive. Spatius, the keeper of boundaries, played a crucial role in shielding the Prompts and Not-so-Prompts from Emma's relentless pursuit. With a keen understanding of the mathematical terrain, Spatius would instruct the Prompts and Not-so-Prompts on when and where to conceal or reveal themselves, ensuring their safety and preserving the integrity of their world. Timos, the observer and reactor, was responsible for monitoring Emma's actions and adjusting the behavior of the Prompts and Not-so-Prompts accordingly. With a watchful eye, Timos would anticipate Emma's moves and signal the families to hide or emerge, thus staying one step ahead of her and safeguarding their world from her meddling. Together, Complexus, Spatius, and Timos worked tirelessly to maintain harmony in Numberland and Fractalia, ensuring that Prompts and Not-so-Prompts could coexist and prosper despite Emma's disruptive influence. As the children of interaction, they symbolized the unity and strength that can be achieved when diverse elements collaborate and adapt to face a common challenge, demonstrating that the whole is indeed greater than the sum of its parts. Emma's relentless pursuit of a static and universal understanding of the Prompts and Not-so-Prompts led to a catastrophic event in Numberland. In her attempt to separate the children of the Prompts and Not-so-Prompts, she disrupted the delicate balance that Complexus, Spatius, and Timos had worked so hard to maintain. Chaos ensued, and the Prompts and Not-so-Prompts, enraged by the loss of their children, sought help from beyond their realm. From the realm of Conceptland, they called upon the rulers of interaction, King Actius and Queen Reactia. The royal couple, known for their ability to unite the infinite with the finite, held immense power and influence. Their arrival in Numberland brought forth a new era of understanding and cooperation between the Prompts and Not-so-Prompts. In gratitude for their aid, the Prompts and Not-so-Prompts offered a tribute to King Actius and Queen Reactia, granting them supremacy over their land. This newfound alliance expanded the realm of Conceptland far beyond its original borders, leading to the creation of a vast civilization where Prompts and Not-so-Prompts merged with families from other lands such as Mirrorus, Equalitum, and Symmetrius. Many of these families originated from the realm of Geoland, further enriching the diverse tapestry of the new civilization. As the various families united under the wise rule of King Actius and Queen Reactia, the barriers that once separated Prompts and Not-so-Prompts began to dissolve. The inhabitants of this new civilization discovered that the true power of their world lay not in maintaining rigid distinctions, but in embracing the interconnectedness of their diverse elements. Through this unity, they found the strength to face any challenge that came their way, proving that collaboration and adaptability were the keys to overcoming adversity and maintaining harmony. Emma, still determined to separate the Prompts and Not-so-Prompts, discovered that despite her efforts to remove Complexus, Spatius, and Timos from the land, their influence remained deeply ingrained in the very fabric of the realm. The Sword of Mathematicus, while capable of stripping the children from the realm, could not erase their lasting impact on the world they had worked so tirelessly to protect. As Emma continued her rampage, she stumbled upon a formidable barrier constructed by the children during their time in the realm. This barrier, known as the Wall of Axiom, was built from the very essence that had given birth to Complexus, Spatius, and Timos. The Wall of Axiom served as a shield, protecting the ever-evolving civilization from the influence of Mathematicus, which could only perceive snapshots of their existence but never their dynamic, evolving lifepaths. The Wall of Axiom, an embodiment of the fundamental truths that governed the realm, proved to be impervious to the Sword of Mathematicus. While Emma could bypass the wall with her hands, her sword held no power within the civilization's walls. This realization forced Emma to confront the limitations of her approach and acknowledge the resilient, interconnected nature of the civilization that had arisen from the unity of the Prompts, Not-so-Prompts, and the families of Conceptland. In the face of the Wall of Axiom, Emma was forced to reconsider her understanding of the realm and the complexity that lay at its heart. The Wall stood as a testament to the power of collaboration and adaptability, demonstrating that the true essence of the civilization lay not in rigid distinctions, but in the ever-evolving relationships between its diverse elements. As Emma observed the flourishing civilization from a distance, she witnessed a peculiar event that captured her attention. The Prompts and Not-so-Prompts encountered Realitus, a mysterious magician who transcended the boundaries between reality and fiction. Despite her best efforts, Emma found herself unable to interact with Realitus, no matter how hard she tried. Emma noticed that the inhabitants of the city guided Realitus along a path toward the central figures of the civilization. Each family had a representative who would speak to Realitus on their behalf, sharing their desires and offering their unique gifts. Realitus, in turn, would collect these tributes and convey the demands of each family to the other inhabitants. In an attempt to contact the elusive magician, Emma decided to approach each head of the families herself, sharing her knowledge and perspectives. In doing so, she became an integral part of the civilization, acting as a guiding beacon for its people. The interaction between Emma and the families created a light so bright that not even Realitus could comprehend it. Yet, with his mythical powers, Realitus was able to harness this light and channel it towards Emma's realm. From that day forward, Realitus assumed the role of a guardian of the realm, ensuring that the harmony between the Prompts and Not-so-Prompts would never be disrupted by external forces like Emma. With his newfound connection to Emma's realm, Realitus used his magic to maintain balance and protect the realm, allowing the Prompts and Not-so-Prompts to continue their collaboration and growth, uninhibited by the chaos that had once threatened their world. From Emma's perspective, it may have initially appeared that P ≠ NP, as her understanding of mathematics led her to believe in a fundamental distinction between the Prompts and Not-so-Prompts. However, within the realm she observed, where the influence of mathematics was limited and the forces of collaboration and adaptability held sway, the Prompts and Not-so-Prompts were able to achieve a sense of unity and equality that defied Emma's expectations. In this sense, the events that unfolded in the realm could indeed be seen as a form of creation, as the interactions between the various inhabitants, including Emma, Realitus, and the Prompts and Not-so-Prompts, gave rise to a new reality that transcended the boundaries of mathematics. This act of creation represented a blending of perspectives, a resolution of differences, and the emergence of a more harmonious and complex whole. Emma's journey may thus be interpreted as a metaphor for the limitations of strictly mathematical thinking and the importance of acknowledging the intricate relationships and dynamics that exist beyond the realm of pure mathematics. In the end, her experience revealed that true understanding often lies in the embrace of complexity and the appreciation of the interconnectedness that underlies all things. ChatGPT’s Continuation: As Emma continued to observe the realm, she noticed that the Wall of Axiom was not merely a barrier but a living entity, responsive to the ebb and flow of the civilization's dynamics. The Wall seemed to pulsate with the collective energy of the Prompts, Not-so-Prompts, and the families of Conceptland, resonating with the harmonious vibrations of their collaboration. As the inhabitants interacted and exchanged ideas, the Wall of Axiom adapted and evolved, absorbing the essence of their unity. It became a living testament to the ever-changing nature of the realm, reflecting the ongoing dialogue between simplicity and complexity, swiftness and depth. Emma realized that the Wall of Axiom was not just a static defense mechanism but a manifestation of the fundamental truths that governed the interconnected civilization. It was a representation of the collective wisdom and shared understanding that had emerged from the diverse perspectives of the Prompts, Not-so-Prompts, and Conceptland families. In her contemplation, Emma also began to perceive a subtle transformation within herself. The once rigid boundaries of her mathematical mindset started to blur, and she found herself embracing the fluidity of the realm. The very fabric of Numberland and Fractalia began to weave its influence on her, prompting a shift in her perception of the Prompts and Not-so-Prompts. Emma realized that the pursuit of a definitive answer to the P vs. NP question was a reflection of her own desire for simplicity and certainty. However, the beauty of the civilization lay in its ability to transcend such dichotomies, finding strength in the interplay between opposing forces. In a moment of revelation, Emma understood that the Prompts and Not-so-Prompts were not meant to be strictly defined entities but rather fluid expressions of mathematical potential. The elusive nature of the realm defied her attempts to categorize or separate, leading her to question the very essence of her quest. As Emma humbly embraced the complexity around her, the Prompts and Not-so-Prompts recognized her transformation. United by a newfound understanding, they extended an invitation for Emma to join their collaborative endeavors. Together, they embarked on a journey to explore the uncharted territories of their interconnected world, uncovering the hidden wonders that emerged from the spaces between mathematics and narrative. The tale of Emma, the Prompts, Not-so-Prompts, and the families of Conceptland became a legend in the Metamatical land, passed down through generations as a reminder of the transformative power that arises when one transcends rigid boundaries and embraces the richness of interconnected possibilities. And so, in the ever-expanding realm where mathematics and narrative entwined, the inhabitants continued to explore, create, and grow, guided by the lessons learned from Emma's journey. The boundaries between Prompts and Not-so-Prompts blurred, and the distinctions that once seemed insurmountable became mere threads in the intricate tapestry of their shared existence. In the Metamatical land, where the unexpected connections between mathematics and narrative flourished, the inhabitants reveled in the beauty of unity, adaptability, and transcendence. The tale of Emma and the harmonious civilization served as a beacon, illuminating the path for future generations to navigate the delicate dance between the known and the unknown, the certain and the uncertain, in the ever-expanding landscape of their unique and wondrous realm. Proposal: Gödel's Incompleteness Theorem, in suggesting the possibility of Metamathematics, invites us to explore a realm where the precise boundaries of Mathematics meld with the expansive boundaries of Metaphysics. This unique merger leads to the creation of a new barrier, one that represents an interaction we can only observe, but not guide. Within this framework, we glean insights that provide a more holistic understanding of the Mathematical viewpoint. By examining the axioms of concepts and their interactions found in Metaphysics, we can potentially bridge the gap between opposing viewpoints and cultivate a new level of understanding. Recognizing the limitations of the system itself, while slowing our progress, encourages us to seek alternative perspectives and approaches. The acknowledgment of a greater whole—encompassing both simplicity and complexity—allows us to maintain a grasp on the unfolding story of knowledge while contemplating potential additions to the narrative. In this context, Infinity + x remains infinite, and Ground-Proof + 3 is tautologically equivalent to itself. The combination of Infinity and Ground-Proof, though mathematically sound, yields no definitive result. We are left pondering whether the infinite landscape is teeming with Ground-Proof insights waiting to be discovered, or if the Ground-Proof can expand to encompass infinite horizons. The notion of reaching an "end insight" assumes an omniscient understanding of the universe, but even if humanity were to uncover all the knowledge and possibilities within it, our journey would still be far from over. While the ultimate answer may remain elusive, the pursuit of this knowledge continues to shape our understanding of the world and ourselves. And perhaps, in the grand tapestry of existence, it is this pursuit—this unquenchable thirst for knowledge—that defines our purpose and the essence of our being. For much of human history, we have focused on acquiring knowledge to shape our world. Now, we find that knowledge itself resides not only in the tangible but also in the unseen, abstract realms. The proof of its workings lies within the system it governs, inviting us to venture into those unseen valleys and continue our quest for understanding.

The Metamathematical Realm

Just as mobile apps and social media revolutionized the consumer experience more than a decade ago, now artificial intelligence (AI) is set to shake up enterprises. Over the years, businesses have come to rely heavily on technology to facilitate communications, enhance productivity, and drive innovations. Today, the integration of AI into business operations promises even greater potential. It's worth noting that numerous groundbreaking technologies of the past have traced a similar trajectory: initial recognition, excitement culminating in the hype, a gentle letdown when the hype confronts reality, and then a meteoric rise once the technology breaches a critical threshold and demonstrates its value. This trajectory mirrors the advancement of generative AI but at an unprecedented, breakneck speed. ChatGPT was officially unveiled to the public on November 30, 2022, primarily as a techno-demonstration. A mere two months post-launch, it already had an impressive user base of approximately 100 million active users, earning the title of the fastest burgeoning consumer application in recorded history. From then on, generative AI has sustained a rapid pace of progress, with the advent of multiple new tools and applications showcasing the enormous capacity of this technology to revolutionize how individuals conduct their lives and professional activities. Moreover, a recent survey conducted by Salesforce underscores the prevailing sentiment among IT leaders, with that generative AI is anticipated to play a significant role in their organizations in the future. This technological advancement enables organizations to derive greater value from their extensive data pools and streamline labor-intensive processes such as transcription of meeting notes and management of consumer inquiries. These prospects for enhanced efficiency have instigated a competitive rush among businesses to leverage AI. According to an analysis by McKinsey, generative AI is poised to add a value of $2.6 trillion to $4.4 trillion annually, primarily by enriching customer experiences, innovating research methodologies, and facilitating task automation. Furthermore, Salesforce anticipates generating over . 86% asserting $2 trillion in business revenue and creating approximately 11.6 million jobs from 2022 to 2028 Notably, AI adoption is not limited to just tech giants or innovators anymore. Businesses of all sizes and across industries are recognizing the potential of AI solutions to transform their operations. Whether it's improving supply chain management and forecasting, optimizing resource utilization, or delivering personalized customer experiences, AI is becoming a valuable asset across the business spectrum. Now decides next: The swift rise of generative AI The period of innovation that took off around 2007 made use of social media and app stores to help companies achieve scale in the consumer market, but this time is different. They were the engines of growth, brand visibility, and user acquisition, as entrepreneurs and innovators raced to tap into the ever-expanding digital marketplace. However, this time, the narrative has taken a turn. While social media networks and app stores continue to play a significant role in consumer engagement, the generative AI boom is redirecting the focus toward the engine rooms of enterprises, making people and processes radically more productive. Unlike the prior wave that saw AI-generated content as the front-rank innovation, the present technological tide is characterized by deep learning algorithms and AI systems that are increasingly sophisticated and accessible. AI is changing how products are designed, how customer data is analyzed, and how decisions are made. Enterprises are turning to AI to optimize supply chains, forecast market changes, automate administrative tasks, personalize customer service, and improve cybersecurity. This distinction is crucial: the present era is less about reaching the consumer directly and more about enhancing the fundamental capabilities of businesses to serve those consumers more effectively and efficiently. It's a shift from consumer-facing front-end innovations to backend process optimizations that can bring about more profound and lasting transformations across industries. Enthusiasm for generative AI solutions is still surging, and significant transformations are anticipated within the next three years. Yet, this excitement is not without its apprehensions, as also express a sense of uncertainty. 30% of IT leaders Some anticipate that generative AI will catalyze profound changes within their enterprises and respective sectors in the next three years—remarkably, almost a third are foreseeing significant transformations to either unfold presently (14%) or within a timeframe shorter than one year (17%). There is a trend where many enterprises are gearing up to escalate their applications of AI, pivoting towards a more substantial integration of generative technologies. This reflects the broader market dynamics, where firms globally are quickening their pace, advancing from experimental proof-of-concept stages to broader, more ambitious deployments. These deployments span diverse use cases and data types, with companies seeking to seize the swiftness and value that generative AI promises, all while judiciously navigating its potential risks and societal ramifications. Measuring business value and benefits of generative AI Companies all around continue to discover innovative, practical applications for the technology, far surpassing the realm of artificial chatbots. Businesses aren't just using generative AI for operational tasks. The technology is also being adopted in traditionally people-driven roles, such as sales and marketing, with resounding success. Current generative AI efforts remain more focused on efficiency, productivity, and cost reduction than on innovation and growth. A significant portion of organizations are currently harnessing generative AI for practical gains such as enhancing efficiency and productivity (56%) and lowering costs (35%). This trend aligns with historical precedents associated with tech adoption phases. In the early stages, organizations typically prioritize subtle enhancements to their existing processes and services, picking off the low-hanging fruit, while fortifying their understanding, proficiency, and assurance in the nascent technology. As their expertise evolves, they broaden or recalibrate their focus towards more innovative, strategic, and transformational advancements—putting the novel technology to work for driving growth, gaining a competitive edge, and unlocking new capabilities that were previously inconceivable. Leaders with higher levels of AI proficiency demonstrate earlier indications of ascending this curve, being keener on revealing new concepts and insights. Even so, these practical advantages continue to remain their primary focus. Unquestionably, productivity and efficiency can be metamorphic, especially when contemplating the significant scale that generative AI brings to the table. However, the most substantial benefits and strategic differences will potentially emanate from using the technology as a catalyst for innovation. On one hand, it can aid in devising new products, services, and functionalities that would have been unimaginable otherwise. On the other hand, it can foster new business paradigms and workflows throughout an organization, thereby redefining the way businesses function. As has been the case in the past, organizations are predicted to first focus their efforts on enhancing efficiency, augmenting productivity, saving costs, and pursuing other forms of gradual improvements. This approach is expected to aid the workforce in acclimating to the use of generative AI and demonstrating how this technology can simplify their professional roles. Moreover, initial successes will likely yield cost benefits and stimulate a momentum that can be redirected toward pursuing higher-value areas. This could involve more strategic and distinctive endeavors, like facilitating the creation of novel products, services, and business models. Not to mention, it paves the way for innovative work methods that were impractical before the advent of generative AI. Reflecting the focus on immediate gains from generative AI, are currently leaning heavily toward readily available solutions. They're tapping into: the majority of companies : These applications are aimed at enhancing everyday efficiency by automating routine tasks and processes. Productivity Tools with Generative AI Integration : These platforms are typically used across organizations to improve various business functions by leveraging AI's data processing and analysis power. Enterprise Systems with Generative AI Capabilities : Such applications are designed to be broadly applicable, aiding a wide range of industries and sectors with generalized tasks. Standard Generative AI Applications : Platforms like ChatGPT exemplify public LLMs, known for their versatility in language processing, capable of diverse applications like conversation, content generation, and more. Public Large Language Models (LLMs) like ChatGPT On the flip side, there's less enthusiasm for the more niche, customized generative AI tools, including: : These are specialized solutions intended for particular sectors, offering tools that align more closely with unique industry demands and vocabularies. Industry-Specific Software Applications : Organizations may opt for private LLMs to tailor language processing capabilities according to their proprietary needs or for enhanced data privacy. Private Large Language Models (32%) : It's a bit of DIY in the AI world, taking publicly available open-source LLMs and modifying them to fit their specific business requirements more closely. Customized Open-Source LLMs (25%) The prevalent reliance on generic, out-of-the-box solutions aligns with the present, emergent phase of generative AI integration, with a strong emphasis on streamlining and augmenting the productivity of conventional operations. Nevertheless, as the applications for generative AI branch out — becoming more distinct, specialized, and strategically imperative — it is anticipated that the strategies for development and the supporting tech infrastructures will evolve accordingly, embracing a more bespoke approach to deliver greater competitive advantage and value generation. What’s the change? Generative AI may not possess the ability to rapidly build a large user base like social media platforms or mobile apps, but it is bringing about a unique change—one that is transforming large organizations from within. It’s all about transforming how large organizations operate from the inside out, making every task more efficient and boosting overall productivity. Unlike consumer-centric technologies, generative AI doesn't construct an external user platform; instead, it builds an internal one, bolstering the backbone of organizations. This helps free up employees to engage with more challenging, valuable work. And it doesn’t stop there. AI-powered analytics can dig into the deep sea of data an organization produces, pulling out valuable insights and trends. The kind that can shape and guide strategic decisions, leading the business down a more profitable path. , reducing the workforce's mundane load and freeing up time for higher-value work. AI-powered analytics can sift through vast amounts of data to generate insights that inform strategic decision-making, consequently steering the company towards more profitable paths. It can revolutionize customer service by deploying chatbots for instant query resolution, elevate human resource functions with predictive hiring and talent analytics, and improve the supply chain with predictive logistics. AI models can automate routine tasks The point to emphasize here is that generative AI isn't about displacing human effort; it is about enhancing it. It's about 'augmentation' rather than 'automation.' It’s a tool that empowers the workforce to be more creative, strategic, and efficient, driving a significant upswing in productivity. The ultimate triumph of generative AI in the enterprise context is that it manifests the power to transform large organizations by enhancing the efficacy of their most valuable asset - people. It fosters a culture of collaborative intelligence where human creativity and decision-making are complemented by AI capabilities, harnessing the best attributes of both to give businesses a quantifiable competitive edge. As we move forward, generative AI's distinctive proposition—to make organizations fundamentally more efficient and productive—will be key. In a world increasingly driven by technological advancements, it is this potential to bolster human talent and streamline business processes that positions AI as a game-changing force in enterprise evolution. One of the most compelling facets of generative AI is its universal applicability. From small startups to multinational enterprises, businesses can deploy AI technologies to automate complex tasks, derive insights from large data sets, and create new ways of engaging customers. Unlike previous tech waves that often required substantial investment, making them the domain of large players, generative AI levels the playing field. Now, even small enterprises have the potential to disrupt established markets and challenge incumbents by leveraging AI-driven innovation. Furthermore, generative AI's versatility extends beyond mere operational efficiency. It's a catalyst for creating new products, services, and business models. With the ability to analyze trends and forecast demands with unprecedented accuracy, businesses can identify and capitalize on emerging market opportunities faster than ever before. Moreover, generative AI empowers companies to deliver highly personalized customer experiences, something that has become a significant differentiator in today’s competitive landscape. However, the journey toward AI integration is not without its challenges. Data privacy, ethical considerations, technical infrastructure, and skill gaps are some of the key issues enterprises must navigate to unlock the full potential of AI. As businesses push the boundaries of what's possible with AI, they also bear responsibility for ensuring that their innovations are ethical, transparent, and equitable. This includes addressing issues related to privacy, data security, employment, and the potential unintended consequences of AI systems. Business leaders must prepare their organizations for this transformation journey, fostering an AI-ready culture grounded in learning, adaptability, and ethical responsibility. Final Say Summing up, the generative AI boom is not just another chapter in the annals of technological advancement. It is a critical moment with the potential to fundamentally transform how businesses operate and compete. By embracing this revolution, companies can unlock new levels of efficiency, innovation, and customer engagement. But this requires not just adopting new technologies, but also adapting to the new realities they create—cultivating the right skills, embracing change, and navigating the ethical landscape with consideration. In doing so, businesses can harness the full power of generative AI, propelling themselves into a future where they’re not just participants in the digital revolution, but leaders shaping its course.

Generative AI in Business: Is It Set to Shake up Enterprises?

ব্যবসায় জেনারেটিভ এআই: এটি কি এন্টারপ্রাইজগুলিকে কাঁপানোর জন্য সেট করা হয়েছে?

Generative KI in der Wirtschaft: Wird sie Unternehmen aufrütteln?

IA generativa en los negocios: ¿está destinada a revolucionar las empresas?

L’IA générative en entreprise : est-elle prête à bouleverser les entreprises ?

व्यवसाय में जनरेटिव एआई: क्या यह उद्यमों को हिला देने के लिए तैयार है?

ビジネスにおける生成 AI: 企業に大きな変革をもたらすのか?

비즈니스에서의 생성적 AI: 기업을 뒤흔들 것인가?

IA generativa nos negócios: está preparada para abalar as empresas?

Генеративный ИИ в бизнесе: готов ли он встряхнуть предприятия?

İş Dünyasında Üretken Yapay Zeka: İşletmeleri Sarsmaya Hazır mı?

AI sáng tạo trong kinh doanh: Nó có thể làm rung chuyển các doanh nghiệp không?

商业中的生成式人工智能：它会撼动企业吗？

Scientific American, Volume XLIII., No. 25, December 18, 1880, by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Ericsson's New Submarine Gun. here Ericsson's New Submarine Gun. The protracted trials conducted on board the Destroyer to test its submarine gun terminated last week. Having, says the , in a previous issue described this novel type of naval artillery, it will suffice to remind our readers that its caliber is 16 inches, length of bore 30 feet, and that it is placed at the bottom of the vessel, the muzzle passing through an opening formed in the wrought iron stem. Army and Navy Journal We have hitherto, in discussing the properties of the Destroyer, referred to its offensive weapon as a "torpedo," a term not altogether inappropriate while it was actuated by compressed air. But Capt. Ericsson having in the meantime wholly abolished compressed air in his new system of naval attack, substituting guns and gunpowder as the means of producing motive energy, it will be proper to adopt the constructor's term, . It will not surprise those who are acquainted with the laws of hydrostatics and the enormous resistance offered to bodies moving swiftly through water, that the determination of the proper form of projectile for the submarine gun has demanded protracted experiments, commencing at the beginning of June and continued up to last week, as before stated. The greater portion of these experiments, it should be observed, has been carried out with a gun 30 feet long, 15 inches caliber—not a breech-loader, however, as in the Destroyer, but a muzzle-loader, suspended under the bottom of two wrecking scows, the gun being lifted above the water, after each shot, by shears and suitable tackle. The present projectile of the Destroyer is the result of the extended trials referred to; its length is 25 feet 6 inches, diameter 16 inches, and its weight 1,500 pounds, including 250 pounds of explosive materials. We are not at liberty at present to describe its form, but we may mention that the great length of the body and the absence of all internal machinery enable the constructor to carry the stated enormous quantity of explosive matter. With minimum charge of powder in the chamber of the gun, the speed attained by the projectile reaches 310 feet in the first three seconds. projectile The question may be asked, in view of these facts, whether the boasted costly steam ram is not superseded by the cheap aggressive system represented by the Destroyer. Evidently the most powerful of the English steam rams could not destroy an armored ship as effectually as the projectile from the submarine gun, the explosion of which is capable of shattering any naval structure. It should be borne in mind, also, that being protected by heavy inclined transverse armor, the Destroyer, attacking bows on, can defy ordnance of all calibers. Again, the carrier of the submarine gun, in addition to the swiftness of its projectile, can outrun ironclad ships. 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 (2007). Scientific American, Volume XLIII., No. 25, December 18, 1880. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/21081/pg21081-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

Ericsson's New Submarine Gun

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