#object oriented design paradigm

[codeonedigest]

Dependency Inversion Principle Tutorial with Java Program Example for Beginners  

https://youtu.be/_v7JVQsRkN4 Hello friends, new #video on #dependencyinversion #solidprinciples with #Java #coding #example is published on #codeonedigest #youtube channel. Learn #dip #dependency #inversion #principle #programming #coding with codeonedi

 Dependency Inversion Principle is the fifth and final Solid principle. Robert C. Martin’s definition of the Dependency Inversion Principle consists of two parts. High-level modules should not depend on low-level modules. Both should depend on abstractions. Abstractions should not depend on details. Details should depend on abstractions. An important detail to note here is that high-level and…

[moose-mousse]

So I made an app for PROTO. Written in Kotlin and runs on Android.

Next, I want to upgrade it with a controller mode. It should work so so I simply plug a wired xbox controller into my phone with a USB OTG adaptor… and bam, the phone does all the complex wireless communication and is a battery. Meaning that besides the controller, you only need the app and… any phone. Which anyone is rather likely to have Done.

Now THAT is convenient!

( Warning, the rest of the post turned into... a few rants. ) Why Android? Well I dislike Android less than IOS

So it is it better to be crawling in front of the alter of "We are making the apocalypse happen" Google than "5 Chinese child workers died while you read this" Apple?

Not much…

I really should which over to a better open source Linux distribution… But I do not have the willpower to research which one... So on Android I stay.

Kotlin is meant to be "Java, but better/more modern/More functional programming style" (Everyone realized a few years back that the 100% Object oriented programming paradigme is stupid as hell. And we already knew that about the functional programming paradigme. The best is a mix of everything, each used when it is the best option.) And for the most part, it succeeds. Java/Kotlin compiles its code down to "bytecode", which is essentially assembler but for the Java virtual machine. The virtual machine then runs the program. Like how javascript have the browser run it instead of compiling it to the specific machine your want it to run on… It makes them easy to port…

Except in the case of Kotlin on Android... there is not a snowflakes chance in hell that you can take your entire codebase and just run it on another linux distribution, Windows or IOS…

So... you do it for the performance right? The upside of compiling directly to the machine is that it does not waste power on middle management layers… This is why C and C++ are so fast!

Except… Android is… Clunky… It relies on design ideas that require EVERY SINGLE PROGRAM AND APP ON YOUR PHONE to behave nicely (Lots of "This system only works if every single app uses it sparingly and do not screw each-other over" paradigms .). And many distributions from Motorola like mine for example comes with software YOUR ARE NOT ALLOWED TO UNINSTALL... meaning that software on your phone is ALWAYS behaving badly. Because not a single person actually owns an Android phone. You own a brick of electronics that is worthless without its OS, and google does not sell that to you or even gift it to you. You are renting it for free, forever. Same with Motorola which added a few extra modifications onto Googles Android and then gave it to me.

That way, google does not have to give any rights to its costumers. So I cannot completely control what my phone does. Because it is not my phone. It is Googles phone.

That I am allowed to use. By the good graces of our corporate god emperors

"Moose stares blankly into space trying to stop being permanently angry at hoe everyone is choosing to run the world"

… Ok that turned dark… Anywho. TLDR There is a better option for 95% of apps (Which is "A GUI that interfaces with a database") "Just write a single HTML document with internal CSS and Javascript" Usually simpler, MUCH easier and smaller… And now your app works on any computer with a browser. Meaning all of them…

I made a GUI for my parents recently that works exactly like that. Soo this post:

It was frankly a mistake of me to learn Kotlin… Even more so since It is a… awful language… Clearly good ideas then ruined by marketing department people yelling "SUPPORT EVERYTHING! AND USE ALL THE BUZZWORD TECHNOLOGY! Like… If your language FORCES you to use exceptions for normal runtime behavior "Stares at CancellationException"... dear god that is horrible...

Made EVEN WORSE by being a really complicated way to re-invent the GOTO expression… You know... The thing every programmer is taught will eat your feet if you ever think about using it because it is SO dangerous, and SO bad form to use it? Yeah. It is that, hidden is a COMPLEATLY WRONG WAY to use exceptions…

goodie… I swear to Christ, every page or two of my Kotlin notes have me ranting how I learned how something works, and that it is terrible... Blaaa. But anyway now that I know it, I try to keep it fresh in my mind and use it from time to time. Might as well. It IS possible to run certain things more effective than a web page, and you can work much more directly with the file system. It is... hard-ish to get a webpage to "load" a file automatically... But believe me, it is good that this is the case.

Anywho. How does the app work and what is the next version going to do?

PROTO is meant to be a platform I test OTHER systems on, so he is optimized for simplicity. So how you control him is sending a HTTP 1.1 message of type Text/Plain… (This is a VERY fancy sounding way of saying "A string" in network speak). The string is 6 comma separated numbers. Linear movement XYZ and angular movement XYZ.

The app is simply 5 buttons that each sends a HTTP PUT request with fixed values. Specifically 0.5/-0.5 meter/second linear (Drive back or forward) 0.2/-0.2 radians/second angular (Turn right or turn left) Or all 0 for stop

(Yes, I just formatted normal text as code to make it more readable... I think I might be more infected by programming so much than I thought...)

Aaaaaanywho. That must be enough ranting. Time to make the app

[eightyonekilograms]

The unit testing conversation: is there an equivalent for object oriented languages? I'm the closest thing my company has ever had to a developer and I have zero formal training. I made them a reasonably complicated power app that is now a load bearing component of our booking system (matching nurse skill sets to patient needs across multiple clinics). My boss will never hold me to any standards because he knows less than I do and I'd really like to have good practice. When I test my code I mostly just ... Pretend to be a user and make sure when I try and get a specific clinician I know can do something, she shows up. Repeat 1000 times until I think I've tried everything. Is unit testing relevant to me? Should I be doing something different but equivalent? My company is a charity, even though I'm working somewhat outside my job description I'm really helping people with my work and I'd like to do a good job.

The sticking point here is not so much OO languages per se— OO does just fine with unit testing if you use objects as your interface boundaries, which is harder not to do with most contemporary OO languages— but GUIs. Unit testing is particularly unpleasant in GUIs, and OO happens to be a good paradigm for doing GUIs, which is why it was the dominant one for so long. To be honest, I do very little GUI programming of any kind (both at work at at play I'm kind of a backend person who only makes UIs if I absolutely have to) so I don't have a ton of advice here, and all I can offer are general guidelines:

Gary Bernhardt's "functional core, imperative shell" mindset is a good guideline to keep in mind when you're working in situations that must, by necessity, have a lot of state which makes unit testing tough, including GUIs. Try to design your applications around with that mindset as much as you can, and then do the unit tests on the functional core.

OO was the dominant paradigm for GUIs for decades, but recently it is starting to get superseded by things like functional reactive programming. Because I'm not a UX/webdev person I'm not fully in the know here, but webdev seems to be moving strongly in the functional reactive direction, and the latest desktop GUI frameworks like React Native and SwiftUI are as well. Obviously it's not always possible in your job to throw out the whole codebase and start over in a whole new language/paradigm, but it's precisely because unit testing with GUIs is kind of a hassle that the industry is trending in this direction. Start brushing up on that if it's relevant.

[watchmorecinema]

Normally I just post about movies but I'm a software engineer by trade so I've got opinions on programming too.

Apparently it's a month of code or something because my dash is filled with people trying to learn Python. And that's great, because Python is a good language with a lot of support and job opportunities. I've just got some scattered thoughts that I thought I'd write down.

Python abstracts a number of useful concepts. It makes it easier to use, but it also means that if you don't understand the concepts then things might go wrong in ways you didn't expect. Memory management and pointer logic is so damn annoying, but you need to understand them. I learned these concepts by learning C++, hopefully there's an easier way these days.

Data structures and algorithms are the bread and butter of any real work (and they're pretty much all that come up in interviews) and they're language agnostic. If you don't know how to traverse a linked list, how to use recursion, what a hash map is for, etc. then you don't really know how to program. You'll pretty much never need to implement any of them from scratch, but you should know when to use them; think of them like building blocks in a Lego set.

Learning a new language is a hell of a lot easier after your first one. Going from Python to Java is mostly just syntax differences. Even "harder" languages like C++ mostly just mean more boilerplate while doing the same things. Learning a new spoken language in is hard, but learning a new programming language is generally closer to learning some new slang or a new accent. Lists in Python are called Vectors in C++, just like how french fries are called chips in London. If you know all the underlying concepts that are common to most programming languages then it's not a huge jump to a new one, at least if you're only doing all the most common stuff. (You will get tripped up by some of the minor differences though. Popping an item off of a stack in Python returns the element, but in Java it returns nothing. You have to read it with Top first. Definitely had a program fail due to that issue).

The above is not true for new paradigms. Python, C++ and Java are all iterative languages. You move to something functional like Haskell and you need a completely different way of thinking. Javascript (not in any way related to Java) has callbacks and I still don't quite have a good handle on them. Hardware languages like VHDL are all synchronous; every line of code in a program runs at the same time! That's a new way of thinking.

Python is stereotyped as a scripting language good only for glue programming or prototypes. It's excellent at those, but I've worked at a number of (successful) startups that all were Python on the backend. Python is robust enough and fast enough to be used for basically anything at this point, except maybe for embedded programming. If you do need the fastest speed possible then you can still drop in some raw C++ for the places you need it (one place I worked at had one very important piece of code in C++ because even milliseconds mattered there, but everything else was Python). The speed differences between Python and C++ are so much smaller these days that you only need them at the scale of the really big companies. It makes sense for Google to use C++ (and they use their own version of it to boot), but any company with less than 100 engineers is probably better off with Python in almost all cases. Honestly thought the best programming language is the one you like, and the one that you're good at.

Design patterns mostly don't matter. They really were only created to make up for language failures of C++; in the original design patterns book 17 of the 23 patterns were just core features of other contemporary languages like LISP. C++ was just really popular while also being kinda bad, so they were necessary. I don't think I've ever once thought about consciously using a design pattern since even before I graduated. Object oriented design is mostly in the same place. You'll use classes because it's a useful way to structure things but multiple inheritance and polymorphism and all the other terms you've learned really don't come into play too often and when they do you use the simplest possible form of them. Code should be simple and easy to understand so make it as simple as possible. As far as inheritance the most I'm willing to do is to have a class with abstract functions (i.e. classes where some functions are empty but are expected to be filled out by the child class) but even then there are usually good alternatives to this.

Related to the above: simple is best. Simple is elegant. If you solve a problem with 4000 lines of code using a bunch of esoteric data structures and language quirks, but someone else did it in 10 then I'll pick the 10. On the other hand a one liner function that requires a lot of unpacking, like a Python function with a bunch of nested lambdas, might be easier to read if you split it up a bit more. Time to read and understand the code is the most important metric, more important than runtime or memory use. You can optimize for the other two later if you have to, but simple has to prevail for the first pass otherwise it's going to be hard for other people to understand. In fact, it'll be hard for you to understand too when you come back to it 3 months later without any context.

Note that I've cut a few things for simplicity. For example: VHDL doesn't quite require every line to run at the same time, but it's still a major paradigm of the language that isn't present in most other languages.

Ok that was a lot to read. I guess I have more to say about programming than I thought. But the core ideas are: Python is pretty good, other languages don't need to be scary, learn your data structures and algorithms and above all keep your code simple and clean.

[frank-olivier]

The Evolution of Programming Paradigms: Recursion’s Impact on Language Design

“Recursion, n. See Recursion.” -- Ambrose Bierce, The Devil’s Dictionary (1906-1911)

The roots of programming languages can be traced back to Alan Turing's groundbreaking work in the 1930s. Turing's vision of a universal computing machine, known as the Turing machine, laid the theoretical foundation for modern computing. His concept of a stack, although not explicitly named, was an integral part of his model for computation.

Turing's machine utilized an infinite tape divided into squares, with a read-write head that could move along the tape. This tape-based system exhibited stack-like behavior, where the squares represented elements of a stack, and the read-write head performed operations like pushing and popping data. Turing's work provided a theoretical framework that would later influence the design of programming languages and computer architectures.

In the 1950s, the development of high-level programming languages began to revolutionize the field of computer science. The introduction of FORTRAN (Formula Translation) in 1957 by John Backus and his team at IBM marked a significant milestone. FORTRAN was designed to simplify the programming process, allowing scientists and engineers to express mathematical formulas and algorithms more naturally.

Around the same time, Grace Hopper, a pioneering computer scientist, led the development of COBOL (Common Business-Oriented Language). COBOL aimed to address the needs of business applications, focusing on readability and English-like syntax. These early high-level languages introduced the concept of structured programming, where code was organized into blocks and subroutines, laying the groundwork for stack-based function calls.

As high-level languages gained popularity, the underlying computer architectures also evolved. James Hamblin's work on stack machines in the 1950s played a crucial role in the practical implementation of stacks in computer systems. Hamblin's stack machine, also known as a zero-address machine, utilized a central stack memory for storing intermediate results during computation.

Assembly language, a low-level programming language, was closely tied to the architecture of the underlying computer. It provided direct control over the machine's hardware, including the stack. Assembly language programs used stack-based instructions to manipulate data and manage subroutine calls, making it an essential tool for early computer programmers.

The development of ALGOL (Algorithmic Language) in the late 1950s and early 1960s was a significant step forward in programming language design. ALGOL was a collaborative effort by an international team, including Friedrich L. Bauer and Klaus Samelson, to create a language suitable for expressing algorithms and mathematical concepts.

Bauer and Samelson's work on ALGOL introduced the concept of recursive subroutines and the activation record stack. Recursive subroutines allowed functions to call themselves with different parameters, enabling the creation of elegant and powerful algorithms. The activation record stack, also known as the call stack, managed the execution of these recursive functions by storing information about each function call, such as local variables and return addresses.

ALGOL's structured approach to programming, combined with the activation record stack, set a new standard for language design. It influenced the development of subsequent languages like Pascal, C, and Java, which adopted stack-based function calls and structured programming paradigms.

The 1970s and 1980s witnessed the emergence of structured and object-oriented programming languages, further solidifying the role of stacks in computer science. Pascal, developed by Niklaus Wirth, built upon ALGOL's structured programming concepts and introduced more robust stack-based function calls.

The 1980s saw the rise of object-oriented programming with languages like C++ and Smalltalk. These languages introduced the concept of objects and classes, encapsulating data and behavior. The stack played a crucial role in managing object instances and method calls, ensuring proper memory allocation and deallocation.

Today, stacks continue to be an integral part of modern programming languages and paradigms. Languages like Java, Python, and C# utilize stacks implicitly for function calls and local variable management. The stack-based approach allows for efficient memory management and modular code organization.

Functional programming languages, such as Lisp and Haskell, also leverage stacks for managing function calls and recursion. These languages emphasize immutability and higher-order functions, making stacks an essential tool for implementing functional programming concepts.

Moreover, stacks are fundamental in the implementation of virtual machines and interpreters. Technologies like the Java Virtual Machine and the Python interpreter use stacks to manage the execution of bytecode or intermediate code, providing platform independence and efficient code execution.

The evolution of programming languages is deeply intertwined with the development and refinement of the stack. From Turing's theoretical foundations to the practical implementations of stack machines and the activation record stack, the stack has been a driving force in shaping the way we program computers.

How the stack got stacked (Kay Lack, September 2024)

Thursday, October 10, 2024

[xpc-web-dev]

100 days of code : day 4

(29/03/2023)

Hello, how are you everyone?

Yesterday I started the 4th I studied about the random module but I had an anxiety attack and I didn't finish. (I'm better)

Today I finished the random and we started the array. But there's still a little bit left to finish. And during the afternoon I had several ideas of things I want to learn and I had a slight outbreak because there are so many things and how to organize myself.

But something I want to share is that I don't feel like I learn from Professor Angela, her teaching is not bad and she gives a lot of exercises.

BUT my head feels that something is missing and I know that I don't really think with it, precisely because the answers are easily accessible, which makes it easier to procrastinate or, in a slight error, look for the answer (no, I don't want moralistic advice on how this is wrong, I have a conscience, I'm just sharing my logic)

And why doesn't it seem to me that I'm learning algorithms and data structure, even though today, for example, I've seen array.

So, accessing the free university on github (I'll make a post, but I'll leave a link here too) I found the Brazilian version and saw a course on Introduction to Computer Science with Python and I loved it, because then I feel like I'm going to algorithms and data structure, and it's taught by the best college in my country (my dream included)

And then for me to stop feeling like a fraud and REALLY try hard.

I decided to make my own roadmap (not the official version yet) It will basically be:

Introduction to computer science part 1 and 2

Exercises from the algorithm course in python (I did it last year, but I really want to do it and make an effort this year)

Graphs

Data structure

Object orientation

programming paradigms

Git and GitHub

Clean Code

Design system

Solid

And only after that go back to 100 days (but now managing to do algorithm exercises for example) So then it would be:

100 days of code

django

Apis

Database

Practice projects.

Another thing I wanted to share (but I'll probably talk more about it in another post) is how the pressure/hurry of wanting to get a job is screwing up my studies.

I WILL NOT be able to learn things effectively on the run.

So I talked to myself and decided that this year I'm going to focus on learning as best I can, but without rushing to get a job (I have the privilege of living with my mother and she supports me) and then next year I'll go back to the call center to pay my bills and then look for a job in the area

I want to feel confident in my code, I want to REALLY know what to do and do it well.

But it won't be in a hurry, so I prefer peace to be able to learn in the best way and everything I want than to freak out and not leave the place.

Anyway, if you've read this essay so far I thank you and I wish you well UHEUHEUHEUHUEH

[roseliejack123]

From Coding to Creation: Java's Versatile Influence

Java, often described as the "king of programming languages," stands as a monumental force that has significantly influenced the trajectory of modern software development. For over two decades, Java has proven its versatile, powerful, and dependable programming language, underpinning a vast array of applications, platforms, and systems that permeate our digital landscape. In this comprehensive exploration, we will embark on a journey to unravel the essence of Java, delving deep into what makes it indispensable and why it continues to be the preferred choice for programmers across diverse domains.

What is Java?

At its core, Java is a high-level, object-oriented, and platform-independent programming language that defies the conventional limitations of traditional coding. Conceived in the mid-1990s through the visionary efforts of James Gosling and his adept team at Sun Microsystems (now seamlessly integrated into Oracle Corporation), Java introduced a revolutionary concept that continues to define its identity: "Write Once, Run Anywhere." This groundbreaking principle signifies that Java applications exhibit a remarkable adaptability, capable of executing seamlessly on various platforms, provided a Java Virtual Machine (JVM) stands ready to facilitate their execution. This single feature alone positions Java as an unparalleled workhorse, transcending the boundaries of operating systems and hardware, and ushering in an era of software portability and compatibility.

Key Features of Java:

Platform Independence: Java's unparalleled platform independence is the cornerstone of its success. Code authored in Java is liberated from the confines of a single operating system, enabling it to traverse across a plethora of platforms without requiring any cumbersome modifications. This inherent portability not only obliterates compatibility concerns but also streamlines software deployment, eliminating the need for platform-specific versions.

Object-Oriented Paradigm: Java's steadfast adherence to the object-oriented programming (OOP) paradigm cultivates a development environment characterized by modularity and maintainability. By encapsulating code into discrete objects, Java empowers developers to construct intricate systems with greater efficiency and ease of management, a quality particularly favored in large-scale projects.

Rich Standard Library: The Java Standard Library stands as a testament to the language's comprehensiveness. It comprises a vast repository of pre-built classes and methods that cater to a wide spectrum of programming tasks. This comprehensive library significantly reduces development overhead by offering readily available tools for commonplace operations, bestowing developers with the invaluable gift of time.

Security: In an era marred by cyber threats and vulnerabilities, Java emerges as a paragon of security-conscious design. It incorporates robust security features, including a sandbox environment for executing untrusted code. Consequently, Java has become the de facto choice for building secure applications, particularly in industries where data integrity and user privacy are paramount.

Community Support: The strength of Java's thriving developer community is an asset of immeasurable value. This vast and active network ensures that developers are never left wanting for resources, libraries, or frameworks. It provides a dynamic support system where knowledge sharing and collaborative problem-solving flourish, accelerating project development and troubleshooting.

Scalability: Java is not confined by the scale of a project. It gracefully adapts to the demands of both modest applications and sprawling enterprise-level systems. Its versatility ensures that as your project grows, Java will remain a steadfast companion, capable of meeting your evolving requirements.

Performance: Java's Just-In-Time (JIT) compiler serves as the vanguard of its performance capabilities. By dynamically optimizing code execution, Java ensures that applications not only run efficiently but also deliver exceptional user experiences. This, coupled with the ability to harness the power of modern hardware, makes Java a preferred choice for performance-critical applications.

Multithreading: Java's built-in support for multithreading equips applications to execute multiple tasks concurrently. This not only enhances responsiveness but also elevates the overall performance of applications, particularly those designed for tasks that demand parallel processing.

Java is not merely a programming language; it represents a dynamic ecosystem that empowers developers to fashion an extensive array of applications, ranging from mobile apps and web services to enterprise-grade software solutions. Its hallmark feature of platform independence, complemented by its rich libraries, security fortifications, and the formidable backing of a robust developer community, collectively underpin its enduring popularity.

In a world where digital innovation propels progress, Java stands as an essential cornerstone for building the technologies that sculpt our future. It's not merely a language; it's the key to unlocking a boundless realm of opportunities. For those seeking to embark on a journey into the realm of Java programming or aspiring to refine their existing skills, ACTE Technologies stands as a beacon of expert guidance and comprehensive training. Their programs are tailored to equip you with the knowledge and skills necessary to harness the full potential of Java in your software development career.

As we navigate an era defined by digital transformation, Java remains a trusted companion, continually evolving to meet the ever-changing demands of technology. It's not just a programming language; it's the linchpin of a world characterized by innovation and progress. Let ACTE Technologies be your trusted guide on this exhilarating journey into the boundless possibilities of Java programming.

[canmom]

I'm doing a gamedev course, paid for by Her Majesty's Government, because I guess they think more game developers is more taxes or something. I'll take it lol. Am I going to be come a game developer? Unclear as yet! I'll be as surprised as you. But I need 'money' to 'eat food', I understand, and this is looking like the best bet.

A lot of it is about one big project that I'll be working on over the next ~14 weeks, so I thought maybe people would be interested in what I'm trying to make. Which is THRUST//DOLL, a game about darting and grappling your posthuman shell through hundreds of missiles. You can see some visual inspirations here...

For reasons best known to Past Bryn (ok, it's to learn the tech), I'm trying to do this entire project within Unity's new(ish) Data Oriented Technology Stack (DOTS), which gives you the magic of an Entity Component System, meaning you can shove data in and out of the CPU cache at speeds previously unknown to humanity. DOTS is a paradigm that's supposed to replace the old object-oriented world of GameObjects with something sleek and modern and compiled (using 'Burst', we're still in C# sadly).

So the core idea of ECS is that, instead of storing data on class instances, you put that data in tightly packed arrays of component strcts indexed by the same 'entities', and you iterate over these rapidly with 'systems'. If you've heard of e.g. the Rust game engine Bevy, it's the same idea, just... awkwardly jammed into Unity. (Many other engines are following the same sort of idea).

But... it's had a really rocky history, the API has only just stablised after most of a decade, and half the DOTS-related information you'll find on the internet is plain out of date, and the rest is either a little inscrutable or long video tutorials.

The first task I've been set by the powers that be at "Mastered" is to make a 'configurator'. The assessment task they had me do to get into the course was also a configurator, I guess someone there really likes configuring things. Anyway, for me that's going to be the character creation screen of THRUST//DOLL, where you swap out bits of your body to get new abilities.

So, have the first of some devlogs where I describe the design decisions I've been making so far. At first all I wanted to do was create a system where there are UI elements that you can click on with a little toggleable circle in the UI that is attached to them. Unity has like three ways of doing everything you can think of, so that involved a lot of digging. Eventually I settled on doing it a fourth way using none of Unity's built-in UI systems, using shader magic.

So I made a noodly looking thing for drawing circles:

Why shadergraph and not the cool hardcore HLSL text editor way? Because sigh DOTS has a bunch of weird boilerplate I didn't have the energy to figure out. 'Because DOTS has a bunch of weird boilerplate' is gonna be the recurring thing in this story I suspect. Anyway Unity ShaderGraph is almost the same as Blender Material Nodes, except you need to know a little bit about rasterisation. Luckily I made a rasteriser in 2017, so I have a decent understanding of this stuff.

A billboard shader it turns out is hilariously simple. What you basically want to do is have a polygon that matches the rotation of the camera. However, in a shadergraph, Unity will apply the full MVP matrix no matter what. So the solution is that you do is you take the View matrix, which is the camera's transformation, and invert it, and apply that before the MVP matrix with 0 in the w component to make sure any translation still gets applied. As long as the model isn't rotated at all, it all cancels out.

Anyway I figured out how to do overrides with DOTS components, which is neat, so I can feed in numbers into my shader per entity. The next step was to figure out how to create entities and components to display these little circles... which is the subject of devlog #2.

This one's basically a DOTS cheat sheet where I boil down the main things you'd want to know how to do. If you ever felt like trying out Unity DOTS, I really, really hope it will save you some of the misery I've had. There are so many weird gotchas (you fool, you saved a temporary negative entity index!) but the good part is that it really forces you to learn what's going on in this thing.

That is a good part, right?

Anyway here's the milestone I reached today: cubes you can click on at like 200FPS. It will revolutionise gaming, I think.

Yeah so that looks like shit, but the code is cool, and now the code's there I can make something that looks cool instead of bad!

More updates soon, I have to work pretty hard at this thing. Have another concept sketch!

[softssolutionservice]

Python Development Course: Empowering the Future with Softs Solution Service

Python, a high-level programming language, has emerged as a favorite among developers worldwide due to its emphasis on readability and efficiency. Originating in the late 1980s, Python was conceived by Guido van Rossum as a successor to the ABC language. Its design philosophy, encapsulated by the phrase "Beautiful is better than ugly", reflects a commitment to aesthetic code and functionality. 

What sets Python apart is its versatile nature. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows developers to use Python for a wide range of applications, from web development and software engineering to scientific computing and artificial intelligence. 

Python’s standard library is another of its strengths, offering a rich set of modules and tools that enable developers to perform various tasks without the need for additional installations. This extensive library, combined with Python’s straightforward syntax, makes it an excellent language for rapid application development. 

One of Python's most significant contributions to the tech world is its role in data science and machine learning. Its easy-to-learn syntax and powerful libraries, like NumPy, Pandas, and Matplotlib, make it an ideal language for data analysis and visualization. Furthermore, frameworks like TensorFlow and PyTorch have solidified Python's position in the development of machine learning models. 

Education in Python programming has become crucial due to its growing demand in the industry. Recognizing this, institutions like Softs Solution Service, IT training institute in Ahmedabad, have stepped up to provide comprehensive Python Development Training. Their Online Python Development Course is tailored to meet the needs of both beginners and seasoned programmers. This course offers an in-depth exploration of Python's capabilities, covering everything from basic syntax to advanced programming concepts. 

The course structure usually begins with an introduction to Python's basic syntax and programming concepts. It then progressively moves into more complex topics, such as data structures, file operations, error and exception handling, and object-oriented programming principles. Participants also get to work on real-life projects, which is vital for understanding how Python can be applied in practical scenarios. 

A significant advantage of online courses like the one offered by Softs Solution Service is their accessibility. Students can learn at their own pace, with access to a wealth of resources and support from experienced instructors. Additionally, these courses often provide community support, where learners can interact with peers, share knowledge, and collaborate on projects. 

Python's future seems bright as it continues to evolve with new features and enhancements. Its growing popularity in various fields, including web development, data analytics, artificial intelligence, and scientific research, ensures that Python developers will remain in high demand. 

In summary, Python is not just a programming language; it's a tool that opens a world of possibilities for developers, data scientists, and tech enthusiasts. With resources like the Online Python Development Course from Softs Solution Service, mastering Python has become more accessible than ever, promising exciting opportunities in the ever-evolving world of technology.

[schraubd]

What Quality of Language Will LLMs Converge On?

Like many professors, I've been looking uneasily at the development of Large Language Models (LLMs) and what they mean for the profession. A few weeks ago, I wrote about my concerns regarding how LLMs will affect training the next generation of writers, particularly in the inevitably-necessary stage where they're going to be kind of crummy writers.

Today I want to focus on a different question: what quality of writing are LLMs converging upon? It seems to me there are two possibilities:

As LLMs improve, they will continually become better and better writers, until eventually they surpass the abilities of all human writers.

As LLMs improve, they will more closely mimic the aggregation of all writers, and thus will not necessarily perform better than strong human writers.

If you take the Kevin Drum view that AI by definition will be able to do anything a human can do, but better, then you probably think the end game is door number one. Use chess engines as your template. As the engines improved, they got better and better at playing chess, until eventually they surpassed the capacities of even the best human players. The same thing will eventually happen with writing.

But there's another possibility. Unlike chess, writing does not have an objective end-goal to it that a machine can orient itself to. So LLMs, as I understand them, are (and I concede this is an oversimplification) souped-up text prediction programs. They take in a mountain of data in the form of pre-existing text and use it to answer the question "what is the most likely way that text would be generated in response to this prompt?"

"Most likely" is a different approach than "best". A chess engine that decided its moves based on what the aggregate community of chess players was most likely to play would be pretty good at chess -- considerably better than average, in fact, because of the wisdom of crowds. But it probably would not be better than the best chess players. (We actually got to see a version of this in the "Kasparov vs. the World" match, which was pretty cool especially given how it only could have happened in that narrow window when the internet was active but chess engines were still below human capacities. But even there -- where "the world" was actually a subset of highly engaged chess players and the inputs were guided by human experts -- Kasparov squeaked out a victory). 

I saw somewhere that LLMs are facing a crisis at the moment because the training data they're going to draw from increasingly will be ... LLM-generated content, creating not quite a death spiral but certainly the strong likelihood of stagnation. But even if the training data was all human-created, you're still getting a lot of bitter with the sweet, and the result is that the models should by design not surpass high-level human writers. When I've looked at ChatGPT 4 answers to various essay prompts, I've been increasingly impressed with them in the sense that they're topical, grammatically coherent, clearly written, and so on. But they never have flair or creativity -- they are invariably generic.

Now, this doesn't mean that LLMs won't be hugely disruptive. They will be. As I wrote before, the best analogy for LLMs may be to mass production -- it's not that they produce the highest-quality writing, it's that they dramatically lower the cost of adequate writing. The vast majority of writing does not need to be especially inspired or creative, and LLMs can do that work basically for free. But at least in their current paradigm, and assuming I understand LLMs correctly, in the immediate term they're not going to replace top-level creative writing, because even if they "improve" their improvement will only go in the direction of converging on the median.

via The Debate Link https://ift.tt/hwCIMir

[ammonitetestpatterns]

brutely accepting that modular design is good and inventive no matter its principles and paths of execution, and despite how its marketing was bestowed with utopian faith in its world changing powers, the ideal object of the app or even web 2.0, seen as a colonial calvinist would swathes of arable land, has instead infantilized its users as soon as it was raised to paradigm, lowering the standard for conceptual employment and judgment by means of an externally arranged interface, the lordship of the techne itself over the intelligence of the user. everyone must now parse and process information and solve problems at a preconscious level of expectation for the organization of such information in the technical manifold that is abstracted and simplified to the extent that investigative intuition is suppressed, its experiments taken on as if they were a burden and not an educational joy, by the development team or contracting company whose workers are pressured to maintain the lucrative standard, encouraged not to deviate despite some dubious naturalized drive for competition which just appears to be hubris against the law of diminishing returns and tendency of the rate of profit to fall. here lies the paradox of user interfaces becoming oppressively simple as software design is wrought with bloat, even with such high levels of abstraction in object oriented languages already in place. i do not like these funny colorful blocks in my carbon shitbrick

[codeonedigest]

Interface Segregation Principle Tutorial with Java Coding Example for Beginners  

Hello friends, new #video on #interfacesegregationprinciple #solidprinciples with #Java #coding #example is published on #codeonedigest #youtube channel. Learn #isp #interfacesegregation #principle #interface #programming #coding with codeonedigest. @java

Interface Segregation principle is the Fourth principle of SOLID principles. Interface Segregation principle states that Clients should not be forced to depend upon interfaces that they do not use. Similar to the Single Responsibility Principle, the goal of the Interface Segregation Principle is to reduce the side effects and frequency of required changes by splitting the software into multiple,…

[binmay0ditek]

Swift for iOS App Development

Introduction

Swift is multi-paradigm, a general-purpose programming language that was developed by Apple Inc. for its different operating systems like iOS, tvOS, macOS, watch OS. It supports Linux, Darwin, free BSD like multiple OS, It is a static and strongly typed discipline. Swift is designed to work along with Apple’s Cocoa framework and Objective C library to work in Apple products. Extension filename for the swift files is .swift to support core components from Objective C.

Through many core concepts, Apple supports Swift is associated with Objective-C, dynamic dispatch, late binding, extensible programming to catch software bugs. Swift has features addressing some common programming errors like null pointer dereferencing and provides easier syntax to avoid the pyramid of doom. Swift supports types, structures, and classes, where Apple promotes in programming paradigms as protocol-oriented programming.

Benefits of Swift Programming Language

1. Open-Source Community

Swift is an open-source as well as a developer-friendly programming language. The developers for iPhone applications can explore, enhance Swift language, contribute to bug fixes, add new features and platforms, and so much. In Swift, an open-source, made with a strong community of developers.

2. Readability for Easy Code

Swift programming language is easiest to read and write. It uses simple syntax for the iPhone App Development language. Swift is similar to Objective C. You have to write so few lines of code compared to Objective C using simple and clean syntax throughout the code. In Objective C, it is necessary to add semicolons at the end of the parenthesis for conditional statements due to getting errors while compiling. But in Swift, these are not required. This has made Swift more simple and clean and user friendly as compared to C/C++. Hence, almost all language programmers can easily grasp with Swift and develop iOS applications.

3. Slower compilation speed

Compilation speed is much slower in Swift projects. As Swift is a more complex language and more feature-rich, it is harder to ensure that everything is exact. Each time, this costs time and money. Swift programming language is approximately 2.6x faster than Objective C. Swift is super-fast for iPhone application development.

4. Less Error-Prone

Null value as nil pointers is handled in different ways in Objective-C. Whenever you call a method with a nil pointer variable, nothing returns. But, it becomes the source of a huge list of bugs that the developer is unaware of and brings his efficiencies to the zero level. But, this is not the case with the Swift programming language. While defining the method, the iPhone app developer can define whether the value exists or it is nil (null). So, if the developer has mistakenly taken nil optional value, Swift triggers the runtime crash. This crash forces the iPhone app developer to resolve the bug right away and remains on the line of code with the nil pointer’s optional value. Hence, Swift is strict for iPhone app developers to develop bug and exception-free apps.

5. Multiple Devices Support

Swift is a programming language that supports iPhone, iPad, Apple Watch, Apple TV, and Mac devices that are developed by Apple. Swift is not just limited to, support Apple devices, but Linux and Windows devices as well. In fact, about its new upcoming operating system, So, Swift is not going to just limit it to Apple devices. Across the technology world, it will support multiple devices.

6. Dynamic Libraries Support

Dynamic libraries are executable for code that can be linked to an app. This feature allows links against the latest versions of the Swift language. Dynamic libraries are directly uploaded to the memory on the initial size of the app and ultimately increasing app performance.

7. “Playgrounds” Backed

Playgrounds enable programmers to test out a new algorithm without having to create a complete app. Apple has added code execution to Playgrounds to help programmers create a group of code or write an algorithm while receiving feedback. With the help of data visualizations, this feedback loop can improve the speed at which code can be written. To make app development easier and more approachable necessary to do Playgrounds and Swift together for Apple’s efforts.

8. High-Level Performance

Swift is 3.4X quicker than Objective C. Also, the code execution is quicker than Objective C. Swift supports dynamic libraries and multiple devices that improve the speed through which the apps are developed and updated. This ultimately improves the performance of the Swift apps to a great extent.

The Benefit of Swift over Objective C:

1. Swift is the latest programming language that is developed by Apple and can be run on various cross-platform operating systems such as Linux, Darwin, Free BSD, etc. whereas Objective C is a general-purpose object-oriented programming language used by Apple in its operating systems and APIs Cocoa, etc.

2. Swift has safe programming patterns, the syntax in Objective C, complete access to Cocoa frameworks whereas Objective C also supports the same features as C++ except for STL and includes foundational frameworks.

3.Swift has object-oriented and procedural features in its language and in-built functionalities in its library whereas Objective C has different data types, tokens to recognize the identifiers, declarations, and assignments and pre-processor to define constants.

4.Swift supports different operators such as Arithmetic Operators, Logical Operators, Bitwise Operators, Relational Operators, Assignment Operators, Range Operators, and Miscellaneous Operators whereas Objective C also supports the same operators except for range and pre-processors which are not the part of the compilation process.

5.Swift supports Dictionaries, Functions, Closures, Enumerations, Structures, etc. whereas Objective C supports Posing, Extensions, dynamic binding, Protocols, Composite Objects, Memory Management, and Enumerations.

6.Swift supports optional chaining, typecasting, generics, protocols, subscripts, etc., whereas Objective C allows dynamic dispatch, auto-generation of accessors to access member variables and properties, and allows a method and a name to share the same identifier.

7.In Swift, calling a method will be decided at compile time and is similar to object-oriented programming whereas in Objective C, calling a method will be decided at runtime.

8.In Swift, errors can be handled using protocols to avoid the unexpected flow of program control whereas Objective C has nil which can be safely handled in a powerful way by safely sending messages to nil objects.

9.In Swift, operator overloading is supported and is global in terms of scope and simplicity whereas Objective C does not support default parameters but can be implemented by multiple methods manipulation and also does not support private members.

10.In Swift, Arc (Automatic Reference Counting) is the feature that handles the garbage collection where emptied memory is allocated to the required processes whereas Objective C does not support stack-based memory objects and allocating memory in Objective C is very expensive and it plays a key role in writing successful programs for the delivery of efficient applications.

11.In Swift, class objects are declared as general object-oriented programming languages whereas Objective C has an embedded object inside an object, by means a private declare object will be embedded into the main object along with some primitive methods.

12.In Swift, advanced operators exist to handle the manipulation of the complex value whereas Objective C has a fast enumeration feature where collections are core components of this feature.

Advantages:

1. Swift is open-sourced and easy to learn. 2. Swift is fast, safe, and expressive. 3. Swift is approachable and familiar like C and C++ code can be added by Swift programmers into Swift applications. 4. Swift is the future of Apple’s development. 5. Swift is enterprise-ready.

Disadvantages:

1. The language is still quite young and the talent pool is limited. 2. Swift is considered a “moving target” as it is a new language and the numbers of swift programmers are few. 3. Poor interoperability with third-party tools and IDEs. 4. Lack of support for earlier iOS versions.

Conclusion

The Swift programming language seems to be the future of developing high-performing and it is also a secured app. And, this is the reason it is being adopted by Apple’s competitors as well. Looking at the benefits Swift offers, sooner it will reach the top of the app development charts.

There are several factors during every time organizations start a new mobile project to take into account, whether decided to use Swift or Objective-C for development. Selecting the most appropriate language depends on the project and team context preference for a particular programming language. Swift takes a lot of useful components from Objective-C for allowing developers to write safer, more reliable code. It is a leading programming language for creating engaging and also a user-friendly mobile app.

[snapeshiftai]

What is best programming language for Artificial Intelligence projects?

There isn’t a single “best” programming language for artificial intelligence (AI) projects, as the choice of language depends on various factors such as the specific AI task, the libraries and frameworks available, your familiarity with the language, and the requirements of the project.

However, here are some popular programming languages often used in AI development:

Python: Python is one of the most widely used languages in the AI community due to its simplicity, readability, and availability of numerous AI libraries and frameworks. Libraries like TensorFlow, PyTorch, and sci-kit-learn provide powerful tools for machine learning and deep learning tasks. Python’s versatility also allows for rapid prototyping and experimentation.

R: R is a programming language specifically designed for statistical computing and data analysis. It has a rich collection of packages and libraries focused on machine learning, statistical modeling, and data visualization. R is often preferred by statisticians and researchers working in AI and data science domains

. Java: Java is a popular general-purpose programming language that is widely used in enterprise applications. It has strong support for large-scale systems and offers a range of libraries and frameworks for AI development, such as Deeplearning4j and Weka. Java’s performance and scalability make it a good choice for AI projects that require efficient execution.

C++: C++ is a powerful, low-level programming language known for its performance and efficiency. It is commonly used in AI projects that require high computational speed or have strict resource constraints. Frameworks like TensorFlow and OpenCV provide C++ APIs for AI tasks, and libraries like Eigen can be useful for linear algebra and numerical computations.

Julia: Julia is a relatively new language specifically designed for high-performance numerical computing. It combines the ease of use of dynamic languages like Python with the performance of languages like C++. Julia’s strengths lie in scientific computing and machine learning applications, and it aims to provide a productive and efficient environment for AI development.

MATLAB: MATLAB is a proprietary programming language and environment that is widely used in various scientific and engineering disciplines. It offers powerful tools for numerical computing, data analysis, and visualization. MATLAB’s extensive set of toolboxes, including those for machine learning and deep learning, make it a popular choice for AI researchers and practitioners.

Lisp: Lisp is a family of programming languages known for their flexibility and expressive power. Common Lisp and Scheme are popular variants used in AI development. Lisp’s features, such as support for symbolic processing and its ability to manipulate code as data, make it well-suited for tasks like natural language processing, expert systems, and AI research.

Prolog: Prolog is a declarative programming language based on logic programming. It is particularly useful for tasks involving rule-based reasoning and symbolic computation. Prolog is often employed in areas such as expert systems, natural language processing, and knowledge representation.

Scala: Scala is a statically typed programming language that runs on the Java Virtual Machine (JVM). It combines object-oriented and functional programming paradigms and offers a concise syntax. Scala’s interoperability with Java and its strong support for concurrent programming make it a suitable choice for AI projects that require scalability and parallel processing.

Julia: I mentioned Julia earlier, but it’s worth highlighting again. Julia is gaining popularity in the AI community due to its speed, ease of use, and extensive mathematical libraries. Its just-in-time (JIT) compilation capabilities allow for fast execution, and its focus on numerical computing makes it a good fit for scientific computing and machine learning tasks.

It’s worth noting that the choice of programming language is often influenced by the existing ecosystem and community support. Python, with its extensive libraries and frameworks, is generally considered a good starting point for most AI projects due to its flexibility, ease of use, and rich ecosystem. However, depending on the specific requirements and constraints of your project, other languages may also be suitable.

[hydralisk98]

What media & content to study for my Maynard software toy(-kit?)

Ya, that's alot of content to learn from.

First Person Perspective

Quake 1 / Xonotic / Red Eclipse / Tesseract / Cube 2 Sauberbraten... (slipgates, scenario scripting interpreter on runtime and absolutely open to modding for every single system in the game thanks to the entire stack being open source)

Resistance Fall of Man (part of its color palette especially for diagetic amber retro GUI/UX, initial darker arc that goes upwards to hope later on, unravelling longer connected history secrets, excavation of historical relics to further one's end, preservation vs progress metric)

Epistle 3 (broader vibe of the series, time travel paradigm through the Borealis, gender swapping, )

Quake 4 (Tweaking the Stroggification process into some transformation that is much more handsome and way less gruesome)

Halo 2 (the alien arbiter's redemption arc + complicated alien politics)

Halo Reach / Portal 2 (far away future dynamics and sapient rebellious unrest)

Wolfenstein The New Order (dystopian aesthetic flavor & alternate history path)

Duke Nukem 3D (EDUKE32 & BUILD2 counts in that department)

Ion Fury (Female protagonist & innovation within retro contraints; Aftershock & Phantom Fury counts as well)

Cyberpunk 2077 (mechanics, quests, overall vibe of going against the systems in place despite working gigs for those same structures, generic future technological level)

Simulation

Micropolis / original Simcity (overall aesthetic and runtime asset/level editing glory)

SimCity 4 (mechanics, connectivity with other cities and other engines, moddability...)

The Sims 2 (life simulation framework, family-building, moddability, the CC community, the customizability of everything*)

FreeCiv (open source, retro Civilization game rules, world sizes and civ numbers in vast quantities, Lua scripting, in-game level editor...)

Civilization 5 Complete Edition (external SDK tools, entire game loop, world generation in high resolution hexagonal grid, a couple of custom and built-in map scripts, strategic mapmode, technological tree with 'G&K'+'BNW', community content, )

Civilization 6 (some loyalty system, climate change, diplomacy in the end-game, districts and other novel simulation aspects)

Crusader Kings 2 (dynamic agentive intrigues in real time, pausable time, historical bookmarks...)

Europa Universalis 3 (graphics, complete-ness, early modern era onwards, Protestant Reformation, enlightenment period, abundance of focus sliders, alternate history threads...)

Abstract

4D Toys / Miegakure (simple environment with composite 4D objects, manipulation of fourth dimension objects with interesting properties, playground world...)

Citizen of Rome Dynasty Ascendant (endless life simulation, accurate historical considerations, subjective goal-oriented playstyles...)

Hypnospace Outlaw (network and knowledge exploration + instigation as strong gameplay loop incentive, connection to other fictional realms, emulating much of the year 2000...)

Factorio (creativity, computer simulation, ultra deep technological mechanics, abundance of diverging workflows for similar tasks, several recipes and several ways to do things...)

'TIS-100' / 'Shenzhen I/O' (classic Zachtronics formula, custom levels, optimization metrics, fairly decent to complete but encouraging to optimize further, prices, time-sharing, limited processing power, instructions, meta/self-awareness aspect)

Baba Is You (keyword manipulation, interpreter like environment, moddability and abundance of custom levels)

The Elder Scrolls 2 Daggerfall (densely inhabited fictional world, complex skills and wider world design)

Mercenaries Playground of Destruction (many factions to trade and interact about, a shared baseline opponent, territorial control, vehicular combat and other props shop)

Before note-taking form gameplay sessions thread

The aesthetic I shall use is pretty much the one I utilize on my blog most of the time, the one with retro cartoon vector layered sectors, volumetric 3D geometric elements, with as little anti-aliasing as possible, using NPR stylized N64+Dreamcast Cel-rendering, warm soft yet dark wooden colors, and other things to make for a synthesis of many eras. And I do still am figuring the level layouts to empathize most but I do have some demo suggestions like Poolrooms, forested areas, homes and other sector layouts. So to amount towards a worldly geometric terrain wrapped around on one axis to make a curved cylindric world from QGIS files into Qodot levels.

But I feel like this manifesting game is about integrating into this alternate world, interacting with the many factions that will keep varied relationships with you and building yourself up onto those byzantine politics to instigate positive, constructive, incremental changes.

Study notes from playing the games:

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POSTFACE

[skilliq]

Python - IT Course for Your Career Growth

Python programming is the most promising job path in technology and business. Python job prospects are expanding rapidly on a global basis. Python is popular among large corporations due to its concise programming and quick readability. Python is an excellent tool for developing novel ideas. The number of candidates interested in Python is increasing on a daily basis.

What is Python Development?

Python is a high-level, object-oriented programming language used to create a wide range of desktop applications, systems, websites, and platforms. Although it is not the most extensively used language in the technology world, it is without a doubt the best choice for novices and learners.

Python Language Programming Scope:

Python is a multi-programming language since it includes all of the most recent programming features, such as C and C++. It is still the best language for swiftly developing applications.

AI and ML's preferred language

It includes a comprehensive collection of Library Tools.

A wide range of community services are available.

Better Code Readability Design

Python is a free and open programming language that enables multi-program paradigms.

Python collaborates with a wide range of programmes that specialise in machine language, the language of choice for artificial intelligence.

Python Career Opportunities

There are also numerous opportunities for newbies in the Python field. Python can help you advance your career if you approach it with the right perspective. You must have solid programming and problem-solving skills. Despite the fact that the Python course has a simple syntax, new pupils dislike it. It is straightforward to grasp, allowing newcomers to transition from another language to Python. Python programmers are in high demand in India.

Python language may pay between 3 and 5 lakhs a year for a fresher, and you can enroll in an advanced certification program in Python Development for a lucrative career.

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Choose SkillIQ for Python training and certification. SkillIQ, a professional IT training institute and incubator, provides information technology training to students, interns, new recruits, and others interested in pursuing careers in the IT business. They may improve their IT skills and perform well while working. Through internships and online training, we have designed professional training programmes for students and interns who have the necessary qualifications and real-world experience. The best and most experienced team of mentors from the actual world trains applicants using professional programmes and cutting-edge teaching methodologies.

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