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.
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.
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.
Common programming paradigms include:
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
Get 20% off Google Google Workspace (Google Meet) Standard Plan with the following codes: 96DRHDRA9J7GTN6 Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more codes)
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.
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.)
In a data-driven world, how we handle data in our apps can make or break them. It can decide if they are robust and scalable or hard to…Continue reading on Medium »
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.
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
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.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with the following codes: 96DRHDRA9J7GTN6 Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more codes)
Hey everyone, I've been encountering a problem when trying to log into ChatGPT. It's frustrating because every time I switch tabs or navigate, I have to log in again. Is anyone else experiencing this issue? I'm really not a fan of these constant interruptions. submitted by /u/Additional-Dog-3337 [link] [comments]
Hello everyone., on today new scraper I created the python version for the zillow scraper. https://github.com/johnbalvin/pyzill What My Project Does The library will get zillow listings and details. I didn't created a defined structured like on the Go version just because it's not as easy to maintain this kind of projects on python like on Go. It is made on pure python with HTTP requests, so no selenium, puppeteer, playwright etc. or none of those automation libraries that I hate. Target Audience This project target could be real state agents probably, so lets say you want to track the real price history of properties around an area, you can use it track it Comparison There are libraries similar outhere but they look outdated, most of the time, scraping projects need to ne on constant maintance due to changed on the page or api pip install pyzill Let me know what ou think, thanks about me: I'm full stack developer specialized on web scraping and backend, with 6-7 years of experience submitted by /u/JohnBalvin [link] [comments]
Weekly Thread: Beginner Questions 🐍 Welcome to our Beginner Questions thread! Whether you're new to Python or just looking to clarify some basics, this is the thread for you. How it Works: Ask Anything: Feel free to ask any Python-related question. There are no bad questions here! Community Support: Get answers and advice from the community. Resource Sharing: Discover tutorials, articles, and beginner-friendly resources. Guidelines: This thread is specifically for beginner questions. For more advanced queries, check out our Advanced Questions Thread. Recommended Resources: If you don't receive a response, consider exploring r/LearnPython or join the Python Discord Server for quicker assistance. Example Questions: What is the difference between a list and a tuple? How do I read a CSV file in Python? What are Python decorators and how do I use them? How do I install a Python package using pip? What is a virtual environment and why should I use one? Let's help each other learn Python! 🌟 submitted by /u/AutoModerator [link] [comments]
I was wondering if anyone has come across anything like https://github.com/a-h/templ or https://hono.dev/guides/jsx, but for python. For context, I am familiar with jinja2, mako, etc. but find them to be unintuitive due to the loose coupling of logic (ex: database calls) and templating (ex: generating a list from the database results). Therefore, I am looking for a "inline" templating solution. submitted by /u/TheRealMrMatt [link] [comments]
I'm trying to decide whether to use typeguard or stick to assert isinstance in the places where I care. Has anyone done benchmarking testing of the overhead of using type guards "at"typeguard decorator ? submitted by /u/gerardwx [link] [comments]
Take the excellent nuitka, compile python code to C, turn it into web assembly, and you got Python in the browser, without the usual runtime overhead: https://wasmer.io/posts/py2wasm-a-python-to-wasm-compiler While the doc states you can get this effect by doing: pip install py2wasm py2wasm myprogram.py -o myprogram.wasm wasmer run myprogram.wasm You still need the wasmer WASM runtime (curl https://get.wasmer.io -sSfL | sh for Unix users, iwr https://win.wasmer.io -useb | iex for Windows user with Powershell), however. But more than that, since you need nuikta, it means you need a C compiler installed. While in Ubuntu it's a just an sudo apt install build-essential, it will require a bit more work on Windows and Mac. submitted by /u/desmoulinmichel [link] [comments]
I'm sharing some of the Python tutorials I made. I teach Ukrainian teenagers and university students Python programming and data science for free. Here is an introduction to Pandas. It shows: How to read data into a Pandas dataframe from a CSV file. Show how to print out the column names. See and set the index. Print sections of the dataframe. Do basic statistics. Create a dataframe from a dictionary. submitted by /u/cyprusgreekstudent [link] [comments]
Weekly Wednesday Thread: Advanced Questions 🐍 Dive deep into Python with our Advanced Questions thread! This space is reserved for questions about more advanced Python topics, frameworks, and best practices. How it Works: Ask Away: Post your advanced Python questions here. Expert Insights: Get answers from experienced developers. Resource Pool: Share or discover tutorials, articles, and tips. Guidelines: This thread is for advanced questions only. Beginner questions are welcome in our Daily Beginner Thread every Thursday. Questions that are not advanced may be removed and redirected to the appropriate thread. Recommended Resources: If you don't receive a response, consider exploring r/LearnPython or join the Python Discord Server for quicker assistance. Example Questions: How can you implement a custom memory allocator in Python? What are the best practices for optimizing Cython code for heavy numerical computations? How do you set up a multi-threaded architecture using Python's Global Interpreter Lock (GIL)? Can you explain the intricacies of metaclasses and how they influence object-oriented design in Python? How would you go about implementing a distributed task queue using Celery and RabbitMQ? What are some advanced use-cases for Python's decorators? How can you achieve real-time data streaming in Python with WebSockets? What are the performance implications of using native Python data structures vs NumPy arrays for large-scale data? Best practices for securing a Flask (or similar) REST API with OAuth 2.0? What are the best practices for using Python in a microservices architecture? (..and more generally, should I even use microservices?) Let's deepen our Python knowledge together. Happy coding! 🌟 submitted by /u/AutoModerator [link] [comments]
Hi there, My team at Bytewax and I have been working on a series of hands-on guides on streaming data and I am excited to share how one can handle missing values in real-time in Python. While some parts of the guide are simplified, for example, we use a random number generator as an input source, the algorithmic part is production-ready. We are taking advantage of the Numpy library + stateful operators in Bytewax. Other input sources are available, too. https://bytewax.io/guides/handling-missing-values submitted by /u/oli_k [link] [comments]
I have started at a new job and had the idea that it would probably be clever to set up my developing environment in exactly the same way as my predecessor did. Because: This should help resolving errors quicker in the transition period His code was good and clean and it appears that he knows what he is doing we were using mostly the same tools (VScode etc.) anyways. He set up his virtual environments (VE)s with conda/mamba. I vaguely remembered that I also used to do that but then stopped for some reason and switched to the virtualenv package. But I did not remember why anymore. So I just set up my VEs in the same way, it should not really make any difference anyways (so I thought). Well, fast forward about two weeks and now I have VEs that occasionally (but not always) exist twice in the same folders under the same name (according to mamba info --envs) and that are at the same time completely empty (according to mamba list) and contain all packages I have installed anywhere, ever (according to pip list). I usually install packages via pip and I assume this may have fucked things up in combination with mamba? I'll probably switch back to virtualenv again and add a "do not use conda/mamba VEs !!!" in my notes. I am working on Windows. Is mamba better on Linux? submitted by /u/RareRandomRedditor [link] [comments]
I found this awesome AI framework for python called "Crew AI", that allows us to create assistants, or agents in the CrewAI lingo, and assign specific tasks. I started my tiny youtube channel 3 months and for a youtuber, especially a novice one like myself, there are a bunch of tasks such as checking what other similar channels are doing, which topics are trending and if a video idea is good or not. There is also the part of coming up with the video content idea itself, create a catchy title, suitable youtube tags and finding the appropriate forum to talk about! You folks get the picture. It's a great project for anyone seeking to automate tasks not just related to a youtube channel, but this could easily be adapted to your daily job tasks, or even a startup idea. I have written a full article about how the project unfolds and also a youtube video for how I did it step by step, which you are welcome to check it out! The links are as follows: The medium article: https://medium.digitalmirror.uk/create-an-ai-team-to-manage-your-youtube-channel-5dc1e6c9b31b The YouTube video: https://youtu.be/5JoVeYcxgpU And of course the source code: https://github.com/fmiguelmmartins/crewaiyoutube.git As always, if you would like to drop some feedback so I can improve with time I would be grateful! Cheers submitted by /u/fx2mx3 [link] [comments]
Just came across this list and I think it's amazing: https://techtalksweekly.substack.com/p/all-pydata-2023-talks submitted by /u/ad81923 [link] [comments]
New developers print out strings to their terminal. It’s how we learn! But printing out to the terminal isn’t what you do with most professional applications. In those cases, you log into files. Sometimes, you log into multiple locations at once. These logs may serve as an audit trail for compliance purposes or help the engineers debug what went wrong. Python Logging teaches you how to log in the Python programming language. Python is one of the most popular programming languages in the world. Python comes with a logging module that makes logging easy. What You’ll Learn In this book, you will learn how about the following: Logger objects Log levels Log handlers Formatting your logs Log configuration Logging decorators Rotating logs Logging and concurrency and more! Book formats The finished book will be made available in the following formats: paperback (at the appropriate reward level) PDF epub The paperback is a 6″ x 9″ book and is approximately 150 pages long. You can check out the book's campaign on Kickstarter. Let me know if you have any questions! submitted by /u/driscollis [link] [comments]
I have not seen updates in almost a year and there is no activity in the support forum? Is there a replacement forum? Thanks submitted by /u/br_web [link] [comments]
https://github.com/leftmove/cria My name is Anonyo, and I am a seventeen year old from Southeast Michigan. This is my second open source project. I built Cria, a Python library that allows you to run LLMs programmatically through Python. Cria is designed so there is as little friction as possible — getting started takes just five lines of code. I created this library because I was using OpenAI in my project, and kept running into rate limits. With local LLMs getting better and better, I sought to switch, but found command line configurations to be limited. Running and configuring LLMs can be trivial, but programs like ollama make it easier. The only problem I found with ollama though, was the lack of features in its Python client. To counteract this, I built Cria — a program that is concise, efficient, and most importantly programmable. The name is based off of Meta's llama, as baby llamas are called crias. Cria solves pain points of ollama - it manages the ollama server for you, saves your message history, streams responses by default, and allows easier management of multiple LLMs at once. The target audience for this project is anyone that uses AI. This library was built in a weekend, and is meant to make running LLMs easier for anyone that wants to go local. I built this for myself, but it could be used at scale as ollama runs its own server (there would of course have to be extra configuration though). In Python, while there are libraries to run LLMs, they are feature limited. Ollama provides the most extensive toolset, and so Cria uses that. Ollama has its own Python library, but I found that to be cumbersome when running multiple LLMs. There are similar libraries, but existing alternatives involve strictly the command line (from what I've seen). While this isn't my first open source project, it's my first Python library. The code may be a little rough around the edges, so I would appreciate any and all suggestions. Thank you! submitted by /u/reformedbillclinton [link] [comments]
Golem Network has just released Ray on Golem MVP, a significant milestone in integrating Ray with Golem's decentralized infrastructure! 🎉 Check out all the details about the release and next steps in our blog post: 📝 https://blog.golem.network/announcing-ray-on-golem-mvp-launch-the-recommended-way-of-running-python-code-on-the-golem-network/ 👉 Golem is looking for engaged beta testers to run diverse applications and collaborate on refining the solution. Interested? Find all the information on the brand-new Ray on Golem site: 🔗 https://www.golem.network/ray submitted by /u/GolemSM [link] [comments]
A project to build a local server without any external dependencies with main features of hot reloading browser on source modification. The idea is to make it simple, standalone and work without any setup. The project uses regular sockets, websockets, inotify and a bit of javascript on the fly embedding to allow users to achieve automatic synchronization with browser. The article describes hot to monitor file changes with direct loading of libc and requesting kernel notify on change. Free article and code: https://hereket.com/posts/linux\_live\_reload\_python\_server/ submitted by /u/tootac [link] [comments]
Hi, I'm Owen, and I've developed an AI tool for data processing. I spend a significant amount of time processing daily data from numerous Excel files, which requires me to write a lot of Python code. I have been using GitHub Copilot to assist with this, but I am exploring ways to eliminate the need to write code manually. The tool is called Tipis AI, and you can find it here: https://github.com/tipisai/tipis-fe What My Project Does Therefore, I have developed an AI that generates Python code for data processing. I've also created a Python runtime environment to execute this code. You can use this tool to process any type of files. Target Audience Any worker. Someone needs to process files. Comparison You can perform complex calculations and summarizations on files using natural language descriptions, without needing to master complex Python knowledge If you have any suggestions for AI in data processing, please let me know, and I might include them in this product.I am very interested in code generation. submitted by /u/smallSohoSolo [link] [comments]
Interactive data visualization is a powerful tool that can significantly enhance the analysis and interpretation of complex datasets. With Python, the Plotly library offers various features that can be used to create interactive publication-quality graphs. This project demonstrates how to use 2 of these awesome features: Range Slider Dropdown Menu A good dataset is essential to demonstrate this style of interactivity. For this project, we will use the UN population projection data to sort and visualize by country for various age groups. FREE ARTICLE: https://johnloewen.substack.com/p/python-plotly-combining-dropdowns-and-range-sliders-for-user-interaction-658dc6fd9c71 submitted by /u/jgloewen [link] [comments]
I made a detailed tutorial on how to work with missing data, outliers, and how to lean up data in Pandas Python. This explains how to clean up your data so that your statistical results and trainin data is not skewed. https://github.com/werowe/HypatiaAcademy/blob/master/pandas/pandas_missing_data.ipynb submitted by /u/cyprusgreekstudent [link] [comments]
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.
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.
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.
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.
Get 20% off Google Google Workspace (Google Meet) Standard Plan with the following codes: 96DRHDRA9J7GTN6 Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more codes)
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.
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 !
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.
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 !
Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
submitted by /u/Cactus_Jacks_Ear [link] [comments]
Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.