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Google’s Carbon Copy: Is Google’s Carbon Programming language the Right Successor to C++?

Carbon Programming language

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Is Google’s Carbon Programming language the Right Successor to C++?

For years, C++ has been the go-to language for high-performance systems programming. But with the rise of multicore processors and GPUs, the need for a language that can take advantage of parallelism has never been greater. Enter Carbon, Google’s answer to the problem. But is it the right successor to C++?

Google has been in the news a lot lately for their new programming language, Carbon. It’s being billed as the successor to C++, but is it really? Let’s take a closer look.

Google's Carbon Copy: Is Google's Carbon Programming language the Right Successor to C++?
Google’s Carbon Copy: Is Google’s Carbon Programming language the Right Successor to C++?

On the surface, Carbon and C++ have a lot in common. They’re both statically typed, object-oriented languages with a focus on performance. They both have a learning curve, but once you know them, you can write code that is both readable and maintainable. However, there are some key differences that make Carbon a more attractive option for modern programmers.

For one, Carbon is garbage collected. This means that you don’t have to worry about manually managing memory, which can be a pain in C++. Carbon also has better support for concurrency than C++. With the rise of multicore processors, this is an important consideration. Finally, Carbon has a more modern standard library than C++. This includes features like string interpolation and pattern matching that make common tasks easier to accomplish.

According to Terry Lambert, Carbon Programming language is probably not the successor of C++. His reason are:

“Single inheritance is a deal-breaker for me, even though the eC++ utilized by IOKit in macOS and iOS has the same restrictions.

Although it specifies stronger type enforcement, which would — in theory — also eliminate RTTI and the reflection, which eC++ has historically eliminated as well, it’s doing it via expression-defined typing, rather than explicitly eliminating it. I expect that it would also prevent use of dynamic_cast, although that’s not explicitly called out.

Let’s see if Linus approves of someone compiling the Linux kernel with Carbon, and then starting to add Carbon syntax code, into that port of Linux.”

On the surface, Carbon seems like a great choice to replace C++. It is designed to be more reliable and easier to use than C++. In addition, it is faster and can be used for a variety of applications. However, there are some drawbacks to using Carbon. First, it is not compatible with all operating systems. Second, it does not have all of the features of C++. Third, it is not as widely used as C++. Finally, it is still in development and has not been released yet.

These drawbacks may seem like deal breakers, but they don’t necessarily mean that Carbon is not the right successor to C++. First, while Carbon is not compatible with all operating systems, it is compatible with the most popular ones. Second, while it does not have all of the features of C++, it has the most important ones. Third, while it is not as widely used as C++, it is gaining popularity rapidly. Finally, while it is still in development, it is expected to be released soon.

What Is Carbon?
Carbon is a statically typed systems programming language developed by Google. It is based on C++ and shares a similar syntax. However, Carbon introduces several new features that make it better suited for parallelism. For example, Carbon provides first-class support for threads and synchronization primitives. It also offers a number of built-in data structures that are designed for concurrent access. Finally, Carbon comes with a toolchain that makes it easy to build and debug parallel programs.

Why Was Carbon Created?
Google’s primary motivation for developing Carbon was to improve the performance of its search engine. To do this, they needed a language that could take advantage of multicore processors and GPUs. C++ was not well suited for this purpose because it lacked support for threading and synchronization. As a result, Google decided to create their own language that would be purpose-built for parallelism.

Is Carbon The Right Successor To C++?
In many ways, yes. Carbon addresses many of the shortcomings of C++ when it comes to parallelism. However, there are some drawbacks. First, Carbon is still in its infancy and lacks many of the features and libraries that have made C++ so popular over the years. Second, because it is designed specifically for parallelism, it may be less suitable for other purposes such as embedded systems programming or network programming. Overall, though, Carbon looks like a promising successor to C++ and is worth keeping an eye on in the future.

Conclusion:
So, is Google’s new Carbon programming language the right successor to C++? We think that Google’s Carbon programming language has the potential to be a great successor to C++.

With its garbage collection, better support for concurrency, and modern standard library, Carbon has everything that today’s programmer needs.

It is designed to be more reliable and easier to use than its predecessor. In addition, it is faster and can be used for a variety of applications. However, there are some drawbacks to using Carbon that should be considered before making the switch from C++.

So if you’re looking for a new language to learn, we recommend giving Carbon a try.


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Programming paradigms 2022-2023

Programming paradigms are a way to classify programming languages based on their features. Languages can be classified into multiple paradigms.

Some paradigms are concerned mainly with implications for the execution model of the language, such as allowing side effects, or whether the sequence of operations is defined by the execution model. Other paradigms are concerned mainly with the way that code is organized, such as grouping a code into units along with the state that is modified by the code. Yet others are concerned mainly with the style of syntax and grammar.


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Common programming paradigms include:

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  • imperative in which the programmer instructs the machine how to change its state,
    • procedural which groups instructions into procedures,
    • object-oriented which groups instructions with the part of the state they operate on,
  • declarative in which the programmer merely declares properties of the desired result, but not how to compute it
    • functional in which the desired result is declared as the value of a series of function applications,
    • logic in which the desired result is declared as the answer to a question about a system of facts and rules,
    • mathematical in which the desired result is declared as the solution of an optimization problem
    • reactive in which the desired result is declared with data streams and the propagation of change

Six programming paradigms that will change how you think about coding

 

Practice Carbon Programming Language at Hackerrank or LeetCode or FreeCodeCamp

Leetcode and HackerRank coding tests don’t work in developer interviews.

Here’s the proof:

Research has shown that work sample tests are VERY effective at determining if someone will we a good fit for a job. But here’s the problem: Work sample tests require applicants to perform tasks or work activities that mirror the tasks employees perform on the job.

When was the last time you had to “reverse an integer” or “find the longest substring without repeating characters”. These types of tests don’t mirror the tasks that software developers perform on the job.

It’s like testing an architect by having them build a house out of playing cards. Leetcode problems are just brain teasers.

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If you want to administer a work sample test, have them do a code review, build a tiny feature in your product, or read and explain some part of your product code. (Every developer knows 90% of your time is spent reading code.)

Developers are tired of Leetcode interviews. It’s time to stop wasting everyone’s time.

Source: https://www.opm.gov/policy-data-oversight/assessment-and-selection/other-assessment-methods/work-samples-and-simulations/

Malbolge 2022 2023

Brooks Otterlake on Twitter: "In case you're curious, this is what a Hello World program looks like in Malbolge. This is the code you would write to display the words "Hello World"

RegEx is just Malbolge for Strings:

r/ProgrammerHumor - RegEx is just Malbolge for strings

What is the hardest programming language? For me, I say C++, C, and Malbolge. Out of all of these, Malbolge is the hardest

Replit Mobile App:  Code on Android and iOS.

Z-Library. The world’s largest ebook library

Top 50 Programming Languages Ranked by the Number of Influenced Languages

Top 50 Programming Languages Ranked by the Number of Influenced Languages
Top 50 Programming Languages Ranked by the Number of Influenced Languages

Programming Breaking News and Quiz

What are the Greenest or Least Environmentally Friendly Programming Languages?

How do we know that the Top 3 Voice Recognition Devices like Siri Alexa and Ok Google are not spying on us?

What are popular hobbies among Software Engineers?

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How do you make a Python loop faster?

How do you make a Python loop faster?

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How do you make a Python loop faster?

Programmers are always looking for ways to make their code more efficient. One way to do this is to use a faster loop. Python is a high-level programming language that is widely used by developers and software engineers. It is known for its readability and ease of use. However, one downside of Python is that its loops can be slow. This can be a problem when you need to process large amounts of data. There are several ways to make Python loops faster. One way is to use a faster looping construct, such as C. Another way is to use an optimized library, such as NumPy. Finally, you can vectorize your code, which means converting it into a format that can be run on a GPU or other parallel computing platform. By using these techniques, you can significantly speed up your Python code.


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According to Vladislav Zorov, If not talking about NumPy or something, try to use list comprehension expressions where possible. Those are handled by the C code of the Python interpreter, instead of looping in Python. Basically same idea like the NumPy solution, you just don’t want code running in Python.

Example: (Python 3.0)

lst = [n for n in range(1000000)]
def loops():
    newlst = []
    for n in lst:
        newlst.append(n * 2)
    return newlst
def lstcomp():
    return [n * 2 for n in lst]
from timeit import timeit
print(timeit(loops, number=100))
#18.953254899999592 seconds
print(timeit(lstcomp, number=100))
#11.669047399991541 seconds
Or Do this in Python 2.0

How do you make a Python loop faster?
How do you make a Python loop faster?


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Python list traversing tip:

Instead of this: for i in range(len(l)): x = l[i]

Use this for i, x in enumerate(l): …

TO keep track of indices and values inside a loop.

Twice faster, and the code looks better.

Another option is to write loops in C instead of Python. This can be done by using a Python extension module such as pyximport. By doing this, programmers can take advantage of the speed of C while still using the convenient syntax of Python.

Finally, developers can also improve the performance of their code by making use of caching. By caching values that are computed inside a loop, programmers can avoid having to recalculate them each time through the loop. By taking these steps, programmers can make their Python code more efficient and faster.

Very Important: Don’t worry about code efficiency until you find yourself needing to worry about code efficiency.

The place where you think about efficiency is within the logic of your implementations.

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This is where “big O” discussions come in to play. If you aren’t familiar, here is a link on the topic

What are the top 10 Wonders of computing and software engineering?

How do you make a Python loop faster?
What are the top 10 most insane myths about computer programmers?

Programming, Coding and Algorithms Questions and Answers

Do you want to learn python we found 5 online coding courses for beginners?

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The Best Python Coding and Programming Bootcamps

We’ve also included a scholarship resource with more than 40 unique scholarships to provide additional financial support.

Python Coding Bootcamp Scholarships

Python Coding Breaking News

  • Relatively new to Python, was inspired by some random math reel to make this
    by /u/Weekly-Food6387 (Python) on September 26, 2023 at 12:05 pm

    https://replit.com/@bossthelegend/SapinskiTriangle?v=1 A cool algorithm that results in infinite triangles, stacked upon one another. submitted by /u/Weekly-Food6387 [link] [comments]

  • Constantly Receiving Python Packages Errors For Almost Every Module
    by /u/Impressive_Ladder249 (Python) on September 26, 2023 at 11:26 am

    Why dose it happen that every module I use I get a thousand lines long error in gibberish about the module I installed that are not my errors, and then it asks me to install another module which either leads to the same problem or that the second module has errors downloading it? I get ImportError and ModuleNotFound errors nonstop, I have the most updated version of pip,I also tried doing the same with pip3, I upgraded (updated) all of the modules I used. I use python 3.11.3 in vs code with an activated virtual environment, I don't use any extension. submitted by /u/Impressive_Ladder249 [link] [comments]

  • New to this world
    by /u/Low-Intern-4039 (Python) on September 26, 2023 at 11:03 am

    Print(“Hello world!”) I’m new to python. I’m taking harvards free cs50p course and am learning as I go. Anyone have some tips for me or wanna share mistakes that have helped you learn to be where you are now? Also… BONUS QUESTION: Do you feel you are capable of coding any app you want if given enough time? submitted by /u/Low-Intern-4039 [link] [comments]

  • Advanced Foundations of Python Programming
    by /u/PangolinSuperb1693 (Python) on September 26, 2023 at 9:45 am

    🚀 Elevate Your Python Skills! Delve into the world of Advanced Foundations of Python Programming and take your coding to the next level. 💡🐍 100% off Udemy course coupon Advanced Foundations of Python Programming Digging Into The Advanced Foundations of Python Programming - Become An Advanced Python Developer - Hands-On Training https://www.korshub.com/courses/advanced-foundations-of-python-programming-complete-training-udemy #udemy #udemyfreecoupon #udemycoupon #udemydiscount #korshub #onlinecourse #freecourse #PythonProgramming #AdvancedSkills submitted by /u/PangolinSuperb1693 [link] [comments]

  • Python Development First Steps and Development IDE Platform
    by /u/PangolinSuperb1693 (Python) on September 26, 2023 at 9:06 am

    🐍 Embark on Your Python Journey! Join our Python Development First Steps course and take your first steps into the world of programming. 💻🚀 100% off Udemy course coupon Python Development First Steps and Development IDE Platform Python Development First Steps and Development IDE Platform Course by MTF Institute https://www.korshub.com/courses/python-development-first-steps-and-development-ide-platform-udemy #udemy #udemyfreecoupon #udemycoupon #udemydiscount #korshub #onlinecourse #freecourse #PythonDevelopment #CodingBeginner submitted by /u/PangolinSuperb1693 [link] [comments]

  • PSA: Python 3.12 releases in one week! NumPy, Pandas and SciPy have 3.12 wheels up on PyPI (among others), get your package ready and wheels uploaded for release!
    by /u/Balance- (Python) on September 26, 2023 at 8:23 am

    Some resources: What’s New In Python 3.12 Python 3.12 release schedule cibuildwheel submitted by /u/Balance- [link] [comments]

  • 🌍 Spatial Analysis of Population Shifts: A Deep Dive into Raster-based Exploration 🌍
    by /u/iamgeoknight (Python) on September 26, 2023 at 6:37 am

    🌍 Spatial Analysis of Population Shifts: A Deep Dive into Raster-based Exploration 🌍 🌍 Spatial Analysis of Population Shifts: A Deep Dive into Raster-based Exploration 🌍 Dive into a comprehensive geospatial analysis of population shifts in Slovakia from 2006 to 2021. This tutorial showcases the power of raster data in identifying significant population changes over time. 📈 Key Takeaways: 🔍 Why rasterizing 1KM Grid Census Data is a game-changer. 🛠️ Step-by-step guide using Python libraries like geopandas, geocube, and xarray. 📌 Pinpointing areas with the most significant population shifts. 📊 Organizing, reprojecting, and saving results for further insights. submitted by /u/iamgeoknight [link] [comments]

  • 82 New Enhancements in NiceGUI: Elevate Your Browser-based Python Frontend
    by /u/r-trappe (Python) on September 26, 2023 at 4:58 am

    It's been four months and 82 new features and enhancements since we last updated you on NiceGUI. We're excited to announce the just released version 1.3.15. NiceGUI is an open-source Python library to write graphical user interfaces which run in the browser. It has a very gentle learning curve while still offering the option for advanced customizations. The library follows a backend-first philosophy: it handles all the web development details. You can focus on writing Python code. Highlights Since 1.2.17 (four month ago) ui.editor: Integrated WSIWIG editor. ui.timeline: Visualize timelines. run.cpu_bound and run.io_bound: simple running of CPU-bound and IO-bound tasks. ui.echart: Apache ECharts integration with click events and dynamic options. ui.json_editor: Edit JSON data directly within the UI. ui.aggrid: Significant improvements for handling complex data structures. ui.scene: Support drag and drop for 3D objects. On-demand loading of dependencies (only the js code needed is send to the browser). Many improvements to Native Mode (eg. electron-like desktop window). Simplify API for registering custom Vue components and JavaScript dependencies. Reduced size of PyPI package by making pywebview, plotly and matplotlib optional. ROS2 integration brings web-ui to your robotics projects. Tech-preview for NiceGUI On Air: public URLs for your local apps. Sample of how to use our new element ui.code to display source code To upgrade: python3 -m pip install --upgrade nicegui For more details, check out our GitHub repository and join r/nicegui. Looking forward to your feedback! submitted by /u/r-trappe [link] [comments]

  • Best front end to use with Python for web app including user file management?
    by /u/Calm-End8700 (Python) on September 26, 2023 at 4:21 am

    I’ve been searching for hours trying to figure this out but what I need to do is create a website which will have a section for users to register/login. Once users are logged in they will have their own dashboard of some sort in which they can upload/delete their files. I currently have working python scripts on my desktop. One of them will extract specific data from pdfs, then save the data into an excel sheet which can be downloaded. I need to put this Python script on the web so I can integrate it into a website and having trouble figuring out the best front end framework to use. Unfortunately, I only know Wordpress, but I also know css, html, and some php. From my reading it seems like React is the number one front end framework to use with Flask but I wanted to ask everyone in here for advice. I plan on using Flask for the backend but hoping there’s a front end which I can use some kind of premade template/theme to get a jump start as I’m not too familiar with react yet (if I use this). I’m looking for something that won’t take me forever to learn but will also work well. Let me know your thoughts and I’m entirely open to suggestions. I’m still relatively new with this so it’s been a bit frustrating but I greatly appreciate any advice you have! submitted by /u/Calm-End8700 [link] [comments]

  • “if not _ in _:”, vs “if _ not in _:”
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    by /u/Im__Joseph (Python) on September 26, 2023 at 12:00 am

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  • yournal.py: Fast (y)ournal script to make daily notes from your terminal.
    by /u/Rikiub (Python) on September 25, 2023 at 8:35 pm

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  • GitHub - Qiskit/rustworkx: A high performance Python graph library implemented in Rust.
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    submitted by /u/pmz [link] [comments]

  • Reproducibility in PyPI
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    submitted by /u/dlorenc [link] [comments]

  • Tutorial: Building a Python Flask Passkey Login Page
    by /u/vdelitz (Python) on September 25, 2023 at 11:59 am

    Hi, I created a step-by-step tutorial that shows how to add passkeys in a Python Flask app. With passkeys, your users can log in via Face ID and Touch ID instead of passwords. The solution in the tutorial: is based on HTML web components (framework-agnostic) uses passwordless email magic links as fallback if the device is not passkey-ready comes with simple session management passkey backend is hosted by Corbado View full tutorial If anyone implemented passkeys already, what was the hardest part? submitted by /u/vdelitz [link] [comments]

  • VLC remote control using FastAPI
    by /u/thirdmanonthemoon (Python) on September 25, 2023 at 11:16 am

    Control your VLC from a different device https://github.com/franciscobmacedo/vlc-remote-control VLC has a web interface to control the playback from the browser. However, the UI is not great and it's not easily customizable. So, I've created a Python app (FastAPI) that lets you control VLC media player through a web interface. You can watch a movie from your couch and control the playback on your phone. Features: Play, pause, volume control, move forward and back, full-screen and quit functionalities. Customizable shortcuts for VLC commands. User-friendly web interface. Currently tested on Mac only. Let me know what you think! submitted by /u/thirdmanonthemoon [link] [comments]

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What are the top 10 most insane myths about computer programmers?

What are the top 10 most insane myths about computer programmers?

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What are the top 10 most insane myths about computer programmers?

Programmers are often seen as a eccentric breed. There are many myths about computer programmers that circulate both within and outside of the tech industry. Some of these myths are harmless misconceptions, while others can be damaging to both individual programmers and the industry as a whole.


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 Here are 10 of the most insane myths about computer programmers:

1. Programmers are all socially awkward nerds who live in their parents’ basements.
2. Programmers only care about computers and have no other interests.
3. Programmers are all genius-level intellects with photographic memories.
4. Programmers can code anything they set their minds to, no matter how complex or impossible it may seem.
5. Programmers only work on solitary projects and never collaborate with others.
6. Programmers write code that is completely error-free on the first try.
7. All programmers use the same coding languages and tools.
8. Programmers can easily find jobs anywhere in the world thanks to the worldwide demand for their skills.
9. Programmers always work in dark, cluttered rooms with dozens of monitors surrounding them.
10. Programmers can’t have successful personal lives because they spend all their time working on code.”

Another Top 10 Myths about computer programmers  in details are:

Myth #1: Programmers are lazy.

This couldn’t be further from the truth! Programmers are some of the hardest working people in the tech industry. They are constantly working to improve their skills and keep up with the latest advancements in technology.


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Myth #2: Programmers don’t need social skills.

While it is true that programmers don’t need to be extroverts, they do need to have strong social skills. Programmers need to be able to communicate effectively with other members of their team, as well as with clients and customers.

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Myth #3: All programmers are nerds.

There is a common misconception that all programmers are nerdy introverts who live in their parents’ basements. This could not be further from the truth! While there are certainly some nerds in the programming community, there are also a lot of outgoing, social people. In fact, programming is a great field for people who want to use their social skills to build relationships and solve problems.

Myth #4: Programmers are just code monkeys.

Programmers are often seen as nothing more than people who write code all day long. However, this could not be further from the truth! Programmers are critical thinkers who use their analytical skills to solve complex problems. They are also creative people who use their coding skills to build new and innovative software applications.

Myth #5: Anyone can learn to code.

This myth is particularly damaging, as it dissuades people from pursuing careers in programming. The reality is that coding is a difficult skill to learn, and it takes years of practice to become a proficient programmer. While it is true that anyone can learn to code, it is important to understand that it is not an easy task.

Myth #6: Programmers don’t need math skills.

This myth is simply not true! Programmers use math every day, whether they’re calculating algorithms or working with big data sets. In fact, many programmers have degrees in mathematics or computer science because they know that math skills are essential for success in the field.

Myth #7: Programming is a dead-end job.

This myth likely comes from the fact that many people view programming as nothing more than code monkey work. However, this could not be further from the truth! Programmers have a wide range of career options available to them, including software engineering, web development, and data science.

Myth #8: Programmers only work on single projects.

Again, this myth likely comes from the outside world’s view of programming as nothing more than coding work. In reality, programmers often work on multiple projects at once. They may be responsible for coding new features for an existing application, developing a new application from scratch, or working on multiple projects simultaneously as part of a team.

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Myth #9: Programming is easy once you know how to do it .

This myth is particularly insidious, as it leads people to believe that they can simply learn how to code overnight and become successful programmers immediately thereafter . The reality is that learning how to code takes time , practice , and patience . Even experienced programmers still make mistakes sometimes !

Myth #10: Programmers don’t need formal education

This myth likely stems from the fact that many successful programmers are self-taught . However , this does not mean that formal education is unnecessary . Many employers prefer candidates with degrees in computer science or related fields , and formal education can give you an important foundation in programming concepts and theory .

Myth #11: That they put in immense amounts of time at the job

I worked for 38 years programming computers. During that time, there were two times that I needed to put in significant extra times at the job. The first two years, I spent more time to get acclimated to the job (which I then left at age of 22) with a Blood Pressure 153/105. Not a good situation. The second time was at the end of my career where I was the only person who could get this project completed (due to special knowledge of the area) in the timeframe required. I spent about five months putting a lot of time in.

Myth #12: They need to know advanced math

Some programmers may need to know advanced math, but in the areas where I (and others) were involved with, being able to estimate resulting values and visualization skills were more important. One needs to know that a displayed number is not correct. Visualization skills is the ability to see the “big picture” and envision the associated tasks necessary to make the big picture correctly. You need to be able to decompose each of the associated tasks to limit complexity and make it easier to debug. In general the less complex code is, the fewer errors/bugs and the easier it is to identify and fix them.

Myth #13: Programmers remember thousands lines of code.

No, we don’t. We know approximate part of the program where the problem could be. And could localize it using a debugger or logs – that’s all.

Myth #14:  Everyone could be a programmer.

No. One must have not only desire to be a programmer but also has some addiction to it. Programming is not closed or elite art. It’s just another human occupation. And as not everyone could be a doctor or a businessman – as not everyone could be a programmer.

Myth #15: Simple business request could be easily implemented

No. The ease of implementation is defined by model used inside the software. And the thing which looks simple to business owners could be almost impossible to implement without significantly changing the model – which could take weeks – and vice versa: seemingly hard business problem could sometimes be implemented in 15 minutes.

Myth #16: Please fix <put any electronic device here>or setup my printer – you are a programmer! 

Yes, I’m a programmer – neither an electronic engineer nor a system administrator. I write programs, not fix devices, setup software or hardware!

As you can see , there are many myths about computer programmers circulating within and outside of the tech industry . These myths can be damaging to both individual programmers and the industry as a whole . It’s important to dispel these myths so that we can continue attracting top talent into the field of programming !

What are the top 10 most insane myths about computer programmers?
What are the top 10 most insane myths about computer programmers?

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