Will No-Code and AI ever make us all software developers?

Will No-Code and AI ever make us all software developers?

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Will No-Code and AI ever make us all software developers?

 In recent years, there has been a growing trend of “no-code” platforms and tools that allow users to create complex applications without a single line of code. At the same time, artificial intelligence is becoming more and more advanced, capable of completing tasks that were once considered impossible for machines to do. So, the question is: will no-code platforms and AI ever make us all software developers? Let’s take a look.

The Pros of No-Code Platforms
No-code platforms have a number of advantages. First and foremost, they lower the barrier to entry for people who want to create software but don’t have the skills or knowledge to do so. Second, they allow for rapid prototyping and iteration; with no need to write code from scratch, you can quickly put together a working prototype of your idea and make changes on the fly. Finally, no-code platforms can be a great way to learn about coding; by using them, you can get a feel for how coding works and what it’s like to work with code, without actually having to write any code yourself.

The Cons of No-Code Platforms
Of course, no-code platforms also have their disadvantages. One major downside is that they can be inflexible; if you want to add a new feature or make a change that’s outside the scope of what the platform allows, you’re out of luck. Additionally, no-code platforms can be expensive; while there are some free options available, many of the best no-code platforms come with a hefty price tag. Finally, because no-code platforms remove the need to write code, they can foster a false sense of security among users who think they know more about coding than they actually do. This can lead to problems down the road if those users ever need to hire a developer or collaborate with one on a project.

The Pros of AI
When it comes to artificial intelligence, there are also some clear advantages. First and foremost, AI is incredibly fast; it can process large amounts of data much faster than any human could hope to. Second, AI is unbiased; because it relies on data instead of human opinion, it can make decisions that are free from personal biases or prejudices. Finally, AI is scalable; as more data is fed into an AI system, it only gets smarter and more accurate over time.

The Cons of AI
AI also has its drawbacks. One major downside is that AI systems require a lot of data to function properly; without enough data points, they simply won’t work. Additionally, AI systems can be opaque; because they rely on complex algorithms that are often inscrutable even to their creators, it can be difficult (if not impossible) to understand how or why an AI system came to a particular decision. Finally, AI systems can be brittle; if something changes in the real world (e.g., a new law is passed), an AI system might not be able to adapt quickly enough and could become obsolete overnight.

The No-Code Movement
No-code platforms like Bubble, Webflow, and Carrd have been gaining in popularity in recent years. And it’s not hard to see why; they allow users to create complex websites and apps without a single line of code required. All you need is a basic understanding of how the platform works, and you can build just about anything you can imagine.

Of course, there are some limitations to what you can do with no-code platforms. They’re not quite as flexible as traditional development environments, so if you want to do something truly unique or complex, you’ll likely need to hire a developer to do it for you. But for most people, no-code platforms are more than sufficient for their needs.

Will No-Code and AI ever make us all software developers?
The Rise of “No Code”. I built my first website with… | by Ryan Hoover | Medium

AI-Powered Development Tools
In addition to no-code platforms, there are also a number of AI-powered development tools that allow non-developers to build software without any coding required. These tools range from simple website builders to full-fledged app development suites. And while they’re not quite as easy to use as no-code platforms, they’re still relatively user-friendly and require no coding knowledge whatsoever.

The Future of Software Development?
So will we all be software developers in the future? It’s hard to say for sure. No-code platforms and AI-powered development tools are certainly making it easier for non-developers to create complex websites and apps. But whether or not that will lead to everyone becoming a developer is impossible to predict. Only time will tell.

This is an interesting take from Harry Dewulf on Quora:

Have you ever heard of Microsoft Query?

Microsoft Query (in several forms) still exists and is still in use today. It’s the living embodiment of the infamous definition of madness oft (wrongly) attributed to Einstein that it’s doing the same thing over and over, and expecting different results.

Will No-Code and AI ever make us all software developers?
Will No-Code and AI ever make us all software developers?

There’s a reason people keep doing the same thing, even though it doesn’t give the intended results. Some strategies just look so much like they will solve the problem that it’s almost impossible to believe that they don’t. So people prefer to disbelieve the results than believe the solution can’t work. This isn’t insanity. It’s a perfectly sensible feature of inductive reasoning which is that the more times you try, the more likely you are to get the result you want. You’d be insane to argue with that. So you have to do quite a difficult (for many people, it seems) piece of reasoning: separate strategies and techniques susceptible to improvement by practice from those that are not.

Of course, in the case of Microsoft Query, there’s a powerful economic motivator.

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People want to believe that you can carry out analytical operations on data without having to use computer code.

The idea of this is just sufficiently removed from simple repetition of the same failed strategy that many people will never realise that they are repeating the same action expecting different results.

The idea is that it’s easier to define complex relationships between data structures visually than verbally.


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The theory being that drawing lines between visual representations of tables is easier than writing the words of an SQL statement.

People who don’t routinely work with data genuinely think that the choice is between the visual and the verbal, and they imagine that data analysts visualize data, so it must surely be easier to represent those visualizations directly, right?

The problem is that although we often call it a visualization, when you are imagining a data structure…

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… okay. This is going to get weird but come with me on it.

The set of all integers is a one-dimensional space.

Add a dimension and you get a graph with coordinates that we normally represent as two numbers. Those two numbers give a location on a flat plane. So you can plot points on a graph, and maybe join them up with your choice of line of best fit.

Add a dimension and you get 3D. You can still just about represent that as an image, if you have a good understanding of the mammalian visual system. You know about perspective, right?

So what if you need a fourth dimension?

Coordinates in 1 dimensional space are expressed like this: 5

Coordinates in 2 dimensional space like this: 2,5

Coordinates in 3 dimensional space like this: 1,2,5
… and if you’re with me so far, you’ll know that the first two numbers refer to locations horizontally, and the third vertically. 3D printers, CAD programs, Blender, 3 point geometry, vectors in three dimensional space, etc.

All this can be represented by images that look meaningful to the human visual system, which is handy, because that’s what they are for.

But typical data structures can have dozens of dimensions.

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Even the simplest ones usually have more than 4. Consider the database that underlies every Enterprise Management System. It has tables for products, clients and orders. You’ve already used up 3 dimensions right there. Supposing you need to create a proposal that demonstrates your ability to provide a subset of your product range to multiple client locations, taking account of vendor and resource availability and seasonal variations? That sort of thing is child’s play compared with evaluating the data from a clinical trial to determine if a new medical device is safe and effective, yet you’re already working with a minimum of five dimensions.

So sure, when thinking about data structures, we frequently “visualize” them, but not as cute 2d images that seem like 3d images “projected inside your mind.” Visualizing data structures so you can design queries for complex datasets is sometimes so difficult it has to be done iteratively. Processing time becomes a factor of query design. You can finish up with a sequence of queries, each of which is multi-dimensional.

Often, the only way to describe them is through the SQL statements that represent them. There will NEVER be a way of representing that as images, because the best that images can ever do is be 2 dimensions that fool your visual system into thinking there are 3 dimensions of space and one of time.

Microsoft Query persists because there will always be people who wishfully think that they can learn to analyse data without learning the “texty part;” without learning to process data (these days, mostly with Python it seems) and without learning to query data with SQL.

No-Code and low-code are wishful thinking except when they are teaching aids (as such, I will admit, they can be a good early stepping stone).

So no, no-code will manacle you, and you’ll love your manacles right up until you need to reach for the door handle.

Conclusion:
So what’s the verdict? Will no-code platforms and AI eventually make us all software developers? It’s hard to say for sure. However, one thing is certain: both no-code platforms and AI have their pros and cons. As such, it’s important to weigh those pros and cons carefully before deciding whether or not either one is right for you.

No one can say for sure whether or not the rise of no-code platforms and AI-powered development tools will ultimately lead to everyone becoming a software developer. But one thing is certain; these trends are making it easier than ever for non-developers to create complex websites and apps without any coding required. So whatever the future may hold, one thing is clear; the landscape of software development is changing, and changing fast.

With the rise of no-code platforms and AI, some people are wondering if we’ll ever see a future where everyone is a software developer. While it’s true that these technologies have made it easier than ever to create digital products, there are still some limitations that prevent them from becoming ubiquitous. In this blog post, we have explored the reasons why no-code and AI probably won’t make us all software developers—at least not anytime soon.

1. No-code platforms still require some technical knowledge.
2. AI is still in its early stages and has a long way to go before it can replace human developers.
3. The demand for software developers is still high, and there aren’t enough no-code/AI solutions to meet that demand.

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

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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.

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.

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

Six programming paradigms that will change how you think about coding

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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.


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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.)

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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:

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

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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.

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?

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.

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

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  • I now know again why I stopped using mamba / conda for setting up virtual environments
    by /u/RareRandomRedditor (Python) on April 22, 2024 at 4:44 pm

    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 made a team of AI manage my YouTube channel and my work with Python based framework CrewAI
    by /u/fx2mx3 (Python) on April 22, 2024 at 2:27 pm

    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]

  • Most-watched PyData conference & meetup talks from 2023
    by /u/ad81923 (Python) on April 22, 2024 at 1:16 pm

    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]

  • Announcing The Python Logging Book & Course Kickstarter
    by /u/driscollis (Python) on April 22, 2024 at 1:06 pm

    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]

  • Is Pythonista (Python for iOS/iPadOS) no longer with active development and support?
    by /u/br_web (Python) on April 22, 2024 at 12:50 pm

    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]

  • I made Cria - Run LLMs (AI) locally and programmatically with as little friction as possible
    by /u/reformedbillclinton (Python) on April 22, 2024 at 12:14 pm

    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]

  • Ray on Golem MVP Launch: the Best Way of Running Python Code on the Golem Network
    by /u/GolemSM (Python) on April 22, 2024 at 10:33 am

    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]

  • Single file, hot reloading python server with raw websockets and inotify
    by /u/tootac (Python) on April 22, 2024 at 9:42 am

    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]

  • I created an AI tool for using python to process excel and other files
    by /u/smallSohoSolo (Python) on April 22, 2024 at 9:40 am

    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]

  • Intermediate Project: Python Plotly Data Visuals: Dropdowns and Range Sliders For User Interaction
    by /u/jgloewen (Python) on April 22, 2024 at 9:18 am

    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]

  • How to Clean Data and work with outliers and Missing Data in Python Pandas
    by /u/cyprusgreekstudent (Python) on April 22, 2024 at 8:11 am

    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]

  • What is currently the fastest/state-of-the-art ODE solver in Python?
    by /u/D_vd_P (Python) on April 22, 2024 at 7:04 am

    For my application, Scipy's solvers are not fast enough so I am looking to speed up by using another package. These are some packages I have found so far: NumbaLSODA DifferentialEquations.jl Torchquad (Although doesn't solve ODEs, just integrates quickly, but that could be wrapped I guess) Torchdiffeq PyDSTool I will be experimenting with these packages, but I was wondering whether anyone has experience with them and has done work on them before. Also, I am wondering whether there are any packages I should check out? submitted by /u/D_vd_P [link] [comments]

  • Tkinter Variable class C/Python garbage collection mismatch
    by /u/ranemaeker (Python) on April 22, 2024 at 4:05 am

    Tkinter is going to drive me crazy. Let me lay this out for you: Tkinter is the GUI interface I am using, which is a python frontend for a C library It holds "widgets" in a window. Some widgets have variables (StringVar, BooleanVar, etc - not "variables" like code variables but a class of updatable data holders for the widgets to link to the Tcl backend) The Vars have incremental names like PYVAR1, PYVAR93, etc by default. Names can be provided to the Vars via keywords upon initialization. (Note I have not been able to find any reason to not do this - hence this post) Names should absolutely not be provided to the Vars unless they are unique. Because on python garbage collection (or "del Var" command) the Vars are told to destroy() themselves and then this command is passed to C "destroy this Var." But the garbage collection in C (or rather Tcl) is slightly delayed from Python... So if I immediately I instantiate a different Var with a different value (or same value, who knows) but reusing the same name, the Var has been deleted in Python (yey!) but it has not been deleted in C yet (uh oh). C says to Python oh hey I got this Var already, use it, don't make a new one. So python goes to use it and then OOPS NEVERMIND LOL IT'S DELETED IN C HAHAHA So now I have this Var that I can't access in Python because it's a Schrodinger's Variable: it both exists and does not exist. Wonky behavior too, like check boxes that are in no state at all, neither checked nor unchecked, and I can't access the variable to see what state it is in because the variable that existed before the one I'm using no longer actually exists. So the Var I can access looks 100% fine! But if I put a value checker in just the right place I can catch where the wonky behavior exists. This has been a frustrating several weeks. Anyway. I have not been able to find any information about this and thought I'd share. Hopefully this helps someone avoid the mistake I made. I have since set all Var names to be the incremental default names. submitted by /u/ranemaeker [link] [comments]

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.

 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.

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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.


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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 !

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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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What are the top 5 common Python patterns when using dictionaries?

What are the top 5 common Python patterns when using dictionaries?

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What are the top 5 common Python patterns when using dictionaries?

In Python, a dictionary is a data structure that allows you to store data in a key/value format. This is similar to a Map in Java. A dictionary is mutable, which means you can add, remove, and update elements in a dictionary. Dictionaries are unordered, which means that the order in which you add elements to a dictionary is not preserved. Python dictionaries are extremely versatile data structures. They can be used to store data in a variety of ways and can be manipulated to perform a wide range of operations.

There are many different ways to use dictionaries in Python. In this blog post, we will explore some of the most popular patterns for using dictionaries in Python.

The first pattern is using the in operator to check if a key exists in a dictionary. This can be helpful when you want to avoid errors when accessing keys that may not exist.

The second pattern is using the get method to access values in a dictionary. This is similar to using the in operator, but it also allows you to specify a default value to return if the key does not exist.

The third pattern is using nested dictionaries. This is useful when you need to store multiple values for each key in a dictionary.

The fourth pattern is using the items method to iterate over the key-value pairs in a dictionary. This is handy when you need to perform some operation on each pair in the dictionary.

The fifth and final pattern is using the update method to merge two dictionaries together. This can be useful when you have two dictionaries with complementary data that you want to combine into one dictionary

1) Creating a Dictionary
You can create a dictionary by using curly braces {} and separating key/value pairs with a comma. Keys must be unique and must be immutable (i.e., they cannot be changed). Values can be anything you want, including another dictionary. Here is an example of creating a dictionary:

“`

python
dict1 = {‘a’: 1, ‘b’: 2, ‘c’: 3}
“`

 

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2) Accessing Elements in a Dictionary
You can access elements in a dictionary by using square brackets [] and the key for the element you want to access. For example:
“`python
print(dict1[‘a’]) # prints 1
“`

If the key doesn’t exist in the dictionary, you will get a KeyError. You can avoid this by using the get() method, which returns None if the key doesn’t exist in the dictionary. For example: “`python print(dict1.get(‘d’)) # prints None “`

If you want to get all of the keys or values from a dictionary, you can use the keys() or values() methods. For example:

“`python
dict = {‘key1′:’value1’, ‘key2′:’value2’, ‘key3′:’value3’}
print(dict[‘key2’]) # Output: value2“`


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““

python keys = dict1.keys() # gets all of the keys

print(keys)
dict_keys([‘a’, ‘b’, ‘c’])

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values = dict1.values() # gets all of the values

print(values)
dict_values([1, 2, 3])

“`

3) Updating Elements in a Dictionary

You can update elements in a dictionary by using square brackets [] and assigning a new value to the key. For example:

“`

python dict1[‘a’] = 10

print(dict1[‘a’]) # prints 10

“`

You can add items to a dictionary by using the update() function. This function takes in an iterable (such as a list, string, or set) as an argument and adds each element to the dictionary as a key-value pair. If the key already exists in the dictionary, then the value of that key will be updated with the new value.

“`python
dict = {‘key1′:’value1’, ‘key2′:’value2’, ‘key3′:’value3’}
dict.update({‘key4′:’value4’, ‘key5’:’value5}) # Output: {‘key1’: ‘value1’, ‘key2’: ‘value2’, ‘key3’: ‘value3’, ‘key4’: ‘value4’, ‘key5’: ‘value5’}“`

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4) Deleting Elements from a Dictionary

You can delete elements from a dictionary by using the del keyword and specifying the key for the element you want to delete. For example:

“`

python del dict1[‘c’]

print(dict1) # prints {‘a’: 10, ‘b’: 2}

“ `

You can remove items from a dictionary by using either the pop() or clear() functions. The pop() function removes an item with the given key and returns its value. If no key is specified, then it removes and returns the last item in the dictionary. The clear() function removes all items from the dictionary and returns an empty dictionary {} .

“`python
dict = {‘key1′:’value1’, ‘key2′:’value2’, ‘key3′:’value3’) dict[‘key1’] # Output: value1 dict[‘key4’] # KeyError >> dict = {}; dict[‘new key’]= “new value” # Output: {‘new key’ : ‘new value’} “`

 

5) Looping Through Elements in a Dictionary

You can loop through elements in a dictionary by using a for loop on either the keys(), values(), or items(). items() returns both the keys and values from the dictionary as tuples (key, value). For example:

“`python for key in dict1: print(“{}: {}”.format(key, dict1[key])) #prints each key/value pair for key, value in dict1.items(): print(“{}: {}”.format(key, value)) #prints each key/value pair #prints all of the values for value in dict1 .values(): print(“{}”.format(value))

6) For iterating around a dictionary and accessing the key and value at the same time:

  • for key, value in d.items(): 
  • …. 
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instead of :

  • for key in d: 
  • value = d[key] 
  • … 

7) For getting a value if the key doesn’t exist:

  • v = d.get(k, None) 

instead of:

  • if k in d: 
  • v = d[k] 
  • else: 
  • v = None 

8) For collating values against keys which can be duplicated.

  • from collections import defaultdict 
  • d = defaultdict(list) 
  • for key, value in datasource: 
  • d[key].append(value) 

instead of:

  • d = {} 
  • for key, value in datasource: 
  • if key in d: 
  • d[key].append[value] 
  • else: 
  • d[key] = [value] 

9) and of course if you find yourself doing this :

  • from collections import defaultdict 
  • d = defaultdict(int) 
  • for key in datasource: 
  • d[key] += 1 

then maybe you need to do this :

  • from collections import Counter 
  • c = Counter(datasource) 

Dictionaries are one of the most versatile data structures available in Python. As you have seen from this blog post, there are many different ways that they can be used to store and manipulate data. Whether you are just starting out with Python or are an experienced programmer, understanding how to use dictionaries effectively is essential to writing efficient and maintainable code.

Dictionaries are powerful data structures that offer a lot of flexibility in how they can be used. By understanding and utilizing these common patterns, you can leverage the power of dictionaries to write more efficient and effective Python code. Thanks for reading!

What are the top 5 common Python patterns when using dictionaries?
What are the top 5 common Python patterns when using dictionaries?

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What are the Greenest or Least Environmentally Friendly Programming Languages?

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Which programming language produces binaries that are the most difficult to reverse engineer?

Which programming language produces binaries that are the most difficult to reverse engineer?

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Which programming language produces binaries that are the most difficult to reverse engineer?

Have you ever wondered how someone might go about taking apart your favorite computer program to figure out how it works? The process is called reverse engineering, and it’s done all the time by software developers in order to learn from other programs or to find security vulnerabilities. In this blog post, we’ll discuss why some programming languages make reverse engineering more difficult than others. We’re going to take a look at why binaries that were originally written in assembly code are generally the most difficult to reverse engineer.

Any given high-level programming language will compile down to assembly code before becoming a binary. Because of this, the level of difficulty in reverse engineering a binary is going to vary depending on the original high-level programming language.

Reverse Engineering

Reverse engineering is the process of taking something apart in order to figure out how it works. In the context of software, this usually means taking a compiled binary and figuring out what high-level programming language it was written in, as well as what the program is supposed to do. This can be difficult for a number of reasons, but one of the biggest factors is the level of optimization that was applied to the code during compilation.

In order to reverse engineer a program, one must first understand how that program was created. This usually involves decompiling the program into its original source code so that it can be read and understood by humans.

Once the source code has been decompiled, a reverse engineer can begin to understand how the program works and look for ways to modify or improve it. However, decompiling a program is not always a trivial task. It can be made significantly more difficult if the program was originally written in a language that produces binaries that are difficult to reverse engineer.

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Some Languages Are More Difficult to Reverse Engineer Than Others.

There are many factors that can make reversing a binary more difficult, but they all stem from the way that the compiled code is organized. For example, consider two different programs written in two different languages. Both programs do the same thing: print “Hello, world!” to the screen. One program is written in C++ and one is written in Java.

When these programs are compiled, the C++ compiler will produce a binary that is considerably smaller than the binary produced by the Java compiler. This is because C++ allows programmers to specify things like data types and memory layout explicitly, whereas Java relies on interpretation at runtime instead. As a result, C++ programs tend to be more efficient than Java programs when compiled into binaries.

However, this also means that C++ binaries are more difficult to reverse engineer than Java binaries. This is because all of the information about data types and memory layout is encoded in the binary itself instead of being stored separately in an interpreted programming language like Java. As a result, someone who wants to reverse engineer a C++ binary would need to spend more time understanding how the compiled code is organized before they could even begin to understand what it does.


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Which programming language produces binaries that are the most difficult to reverse engineer?
Reverse Engineering SOftware

Optimization

Optimization is a process where the compiler tries to make the generated code run as fast as possible, with the goal of making the program take up less memory. This is generally accomplished by reorganizing the code in such a way that makes it harder for a human to read. For example, consider this simple C++ program:

int main() {
int x = 5;
int y = 10;
int z = x + y;
return z;
}
This would compile down to assembly code that looks something like this:

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main: ; PC=0x1001000
mov eax, 5 ; PC=0x1001005
mov ebx, 10 ; PC=0x100100a
add eax, ebx ; PC=0x100100d
ret ; PC=0x100100e
As you can see, even this very simple program has been optimized to the point where it’s no longer immediately clear what it’s doing just by looking at it. If you were trying to reverse engineer this program, you would have a very difficult time understanding what it’s supposed to do just by looking at the assembly code.
Of course, there are ways to reverse engineer programs even if they’ve been heavily optimized. However, all things being equal, it’s generally going to be more difficult to reverse engineer a binary that was originally written in assembly code than one that was written in a higher-level language such as Java or Python. This is because compilers for higher-level languages typically don’t apply as much optimization to the generated code since humans are going to be reading and working with it directly anyways. As a result, binaries that were originally written in assembly tend to be more difficult to reverse engineer than those written in other languages.

Which programming language produces binaries that are the most difficult to reverse engineer?
Thesis Contributions Reverse Engineering

According to Tim Mensch, programming language producing binaries that are the most difficult to reverse engineer are probably anything that goes through a modern optimization backend like gcc or LLVM.

And note that gcc is now the GNU Compiler Collection, a backronym that they came up with after adding a number of frontend languages. In addition to C, there are frontends for C++, Objective-C, Objective-C++, Fortran, Ada, D, and Go, plus others that are less mature.

LLVM has even more options. The Wikipedia page lists ActionScript, Ada, C#, Common Lisp, PicoLisp, Crystal, CUDA, D, Delphi, Dylan, Forth, Fortran, Free Basic, Free Pascal, Graphical G, Halide, Haskell, Java bytecode, Julia, Kotlin, Lua, Objective-C, OpenCL, PostgreSQL’s SQL and PLpgSQL, Ruby, Rust, Scala, Swift, XC, Xojo and Zig.

I don’t even know what all of those languages are. In some cases they may include enough of a runtime to make it easier to reverse engineer the underlying code (I’m guessing the Lisp dialects and Haskell would, among others), but in general, once compiled to a target architecture with maximum optimization, all of the above would be more or less equally difficult to reverse engineer.

Languages that are more rare (like Zig) may have an advantage by virtue of doing things differently enough that existing decompilers would have trouble. But that’s only an incremental increase in difficulty.

There exist libraries that you can add to a binary to make it much more difficult to reverse engineer. Tools that prevent trivial disassembly or that make code fail if run in a debugger, for instance. If you really need to protect code that you have to distribute, then using one of those products might be appropriate.

But overall the only way to be sure that no one can reverse engineer your code (aside from nuking it from orbit, which has the negative side effect of eliminating your customer base) is to never distribute your code: Run anything proprietary on servers and only allow people with active accounts to use it.

Generally, though? 99.9% of code isn’t worth reverse engineering. If you’re not being paid by some large company doing groundbreaking research (and you’re not if you would ask this question) then no one will ever bother to reverse engineer your code. This is a really, really frequent “noob” question, though: Because it was so hard for a new developer to write an app, they fear someone will steal the code and use it in their own app. As if anyone would want to steal code written by a junior developer. 🙄

More to the point, stealing your app and distributing it illegally can generally be done without reverse engineering it at all; I guarantee that many apps on the Play Store are hacked and republished with different art without the thieves even slightly understanding how the app works. It’s only if you embed some kind of copy protection/DRM into your app that they’d even need to hack it, and if you’re not clever about how you add the DRM, hacking it won’t take much effort or any decompiling at all. If you can point a debugger at the code, you can simply walk through the assembly language and find where it does the DRM check—and disable it. I managed to figure out how to do this as a teen, on my own, pre-Internet (for research purposes, of course). I guarantee I’m not unique or even that skilled at it, but start to finish I disabled DRM in a couple hours at most.

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So generally, don’t even bother worrying about how difficult something is to reverse engineer. No one cares to see your code, and you can’t stop them from hacking the app if you add DRM. So unless you can keep your unique code on a server and charge a subscription, count on the fact that if your app gets popular, it will be stolen. People will also share subscription accounts, so you need to worry about that as well when you design your server architecture.

There are a lot of myths and misconceptions out there about binary reversing.

Myth #1: Reversing a Binary is Impossible
This is simply not true. Given enough time and effort, anyone can reverse engineer a binary. It may be difficult, but it’s certainly not impossible. The first step is to understand what the program is supposed to do. Once you have a basic understanding of the program’s functionality, you can start to reverse engineering the code. This process will help you understand how the program works and how to modify it to suit your needs.

Myth #2: You Need Special Tools to Reverse Engineer a Binary
Again, this is not true. All you really need is a text editor and a disassembler. A disassembler will take the compiled code and turn it into assembly code, which is much easier to read and understand.Once you have the assembly code, you can start to reverse engineer the program. You may find it helpful to use a debugger during this process so that you can step through the code and see what each instruction does. However, a debugger is not strictly necessary; it just makes the process easier. If you don’t have access to a debugger, you can still reverse engineer the program by tracing through the code manually.

Myth #3: Only Certain Types of Programs Can Be Reversed Engineered
This myth is half true. It’s certainly easier to reverse engineered closed-source programs than open-source programs because you don’t have access to the source code. However, with enough time and effort, you can reverse engineer any type of program. The key is to understand the program’s functionality and then start breaking down the code into smaller pieces that you can understand. Once you have a good understanding of how the program works, you can start to figure out ways to modify it to suit your needs.

In conclusion,

We can see that binaries compiled from assembly code are generally more difficult to reverse engineer than those from other high-level languages. This is due to the level of optimization that’s applied during compilation, which can make the generated code very difficult for humans to understand. However, with enough effort and expertise, it is still possible to reverse engineer any given binary.

So, which programming language produces binaries that are the most difficult to reverse engineer?

There is no definitive answer, as it depends on many factors including the specific features of the language and the way that those features are used by individual programmers. However, languages like C++ that allow for explicit control over data types and memory layout tend to produce binaries that are more difficult to reverse engineer than interpreted languages like Java.

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 How to find common elements in two unsorted arrays with sizes n and m avoiding double for loop?

How to find common elements in two unsorted arrays with sizes n and m avoiding double for loop? Blog Introduction: In this blog post, we will be discussing how to find common elements in two unsorted arrays with sizes n and m avoiding double for loop. We will be discussing various methods that can be used to solve this problem and comparing the time complexity of each method. Blog Body: Method 1: Linear Search The first method we will discuss is linear search. This method involves iterating through both arrays and comparing each element. If the element is found in both arrays, it is added to the result array. The time complexity of this method is O(nm), where n is the size of the first array and m is the size of the second array. Method 2: HashMap Method The second method we will discuss is the HashMap method. This method involves creating a HashMap of all the elements in the first array. Then, we iterate through the second array and check if the elements are present in the HashMap. If they are, we add them to the result array. The time complexity of this method is O(n+m), where n is the size of the first array and m is the size of the second array. Method 3: Sort andCompare Method The third method we will discuss is the Sort and Compare Method. This method involves sorting both arrays using any sorting algorithm like merge sort or quick sort. Once both arrays are sorted, we compare each element of both arrays one by one until we find a match. If a match is found, we add it to our result array. The time complexity of this method is O(nlogn+mlogm), where n is the size of the first array and m is the size of the second array. Conclusion: In this blog post, we discussed how to find common elements in two unsorted arrays with sizes n and m avoiding double for loop. We discussed three different methods that can be used to solve this problem and compared their time complexities. We hope that this blog post was helpful in understanding how to solve this problem.

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How to find common elements in two unsorted arrays with sizes n and m avoiding double for loop?

Programmers, software engineers, coders, IT professionals, and software architects all face the common challenge of needing to find common elements in two unsorted arrays with sizes n and m. This can be a difficult task, especially if you don’t want to use a double for loop.

In this blog post, we will be discussing how to find common elements in two unsorted arrays with sizes n and m avoiding double for loop. We will be discussing various methods that can be used to solve this problem and comparing the time complexity of each method.

There are several ways that you can find common elements in two unsorted arrays with sizes n and m avoiding double for loop. One way is by using the hashing technique. With this technique, you can create a hash table for one of the arrays. Then, you can traverse through the second array and check if the element is present in the hash table or not. If the element is present in the hash table, then it is a common element. Another way that you can find common elements in two unsorted arrays with sizes n and m avoiding double for loop is by using the sorting technique. With this technique, you can sort both of the arrays first. Then, you can traverse through both of the arrays simultaneously and compare the elements. If the elements are equal, then it is a common element.

Method 1: Linear Search

The first method we will discuss is linear search. This method involves iterating through both arrays and comparing each element. If the element is found in both arrays, it is added to the result array. The time complexity of this method is O(nm), where n is the size of the first array and m is the size of the second array.

Method 2: HashMap Method

The second method we will discuss is the HashMap method. This method involves creating a HashMap of all the elements in the first array. Then, we iterate through the second array and check if the elements are present in the HashMap. If they are, we add them to the result array. The time complexity of this method is O(n+m), where n is the size of the first array and m is the size of the second array.

Method 3: Sort and Compare Method

The third method we will discuss is the Sort and Compare Method. This method involves sorting both arrays using any sorting algorithm like merge sort or quick sort. Once both arrays are sorted, we compare each element of both arrays one by one until we find a match. If a match is found, we add it to our result array. The time complexity of this method is O(nlogn+mlogm), where n is the size of the first array and m is the size of the second array.

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The naïve algorithm for finding common elements in two unsorted arrays with sizes nn and mm is O(nm)O(nm), i.e. quadratic.

The algorithm for sorting an array is O(nlogn)O(nlog⁡n), and you can find common elements in two sorted arrays in O(n+m)O(n+m). In other words, for large enough arrays, it is significantly faster to first sort them, then look for the common elements, because the sorting algorithm will dominate the complexity, so your final algorithm ends up at O(nlogn)O(nlog⁡n) as well.

How to find common elements in two unsorted arrays with sizes n and m avoiding double for loop?
One of the most straight forward ways to find common elements in two arrays is by using a double for loop. This approach is simple to understand and implement but it is not very efficient. The time complexity of this algorithm is O(n*m) where n and m are the size of the two arrays respectively. The reason for this is because we are looping through both arrays completely which takes a lot of time. Furthermore, this approach also uses a lot of extra space because we are storing the common elements in a new list.


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Conclusion:

In this blog post, we discussed how to find common elements in two unsorted arrays with sizes n and m avoiding double for loop. We discussed three different methods that can be used to solve this problem and compared their time complexities. We hope that this blog post was helpful in understanding how to solve this problem.

There are many different ways to find common elements in two unsorted arrays with sizes n and m avoiding double for loop. The most straight forward way is by using a double for loop but this approach is not very efficient. A more efficient way is by using a hash table which has a time complexity of O(n+m). This algorithm is faster because we only need to loop through one of the arrays. We can then use the values from that array to check if there are any duplicates in the second array. This approach also uses less memory because we are not creating a new list to store the common elements.

What are the Top 5 things that can say a lot about a software engineer or programmer’s quality?

https://en.wikipedia.org/wiki/Retrocomputing

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What are the Top 5 things that can say a lot about a software engineer or programmer’s quality?

When it comes to the quality of a software engineer or programmer, there are a few key things that can give you a good indication. First, take a look at their code quality. A good software engineer will take pride in their work and produce clean, well-organized code. They will also be able to explain their code concisely and confidently. Another thing to look for is whether they are up-to-date on the latest coding technologies and trends. A good programmer will always be learning and keeping up with the latest industry developments. Finally, pay attention to how they handle difficult problems. A good software engineer will be able to think creatively and come up with innovative solutions to complex issues. If you see these qualities in a software engineer or programmer, chances are they are of high quality.

Below are the top 5 things can say a lot about a software engineer/ programmer’s quality?

  1. The number of possible paths through the code (branch points) is minimized. Top quality code tends to be much more straight line than poor code. As a result, the author can design, code and test very quickly and is often looked at as a programming guru. In addition this code is far more resilient in Production.
  2. The code clearly matches the underlying business requirements and can therefore be understood very quickly by new resources. As a result there is much less tendency for a maintenance programmer to break the basic design as opposed to spaghetti code where small changes can have catastrophic effects.
  3. There is an overall sense of pride in the source code itself. If the enterprise has clear written standards, these are followed to the letter. If not, the code is internally consistent in terms of procedure/object, function/method or variable/attribute naming. Also indentation and continuations are universally consistent throughout. Last but not least, the majority of code blocks are self-evident to the requirements and where not the case, adequate purpose focused documentation is provided.

    In general, I have seen two types of programs provided for initial Production deployment. One looks like it was just written moments ago and the other looks like it has had 20 years of maintenance performed on it. Unfortunately, the authors of the second type cannot generally see the difference so it is a lost cause and we just have to continue to deal with the problems.
  4. In today’s programming environment, a project may span many platforms, languages etc. A simple web page may invoke an API which in turn accesses a database. For this example lets say JavaScript – Rest API – C# – SQL – RDBMS. The programmer can basically embed logic anywhere in this chain, but needs to be aware of reuse, performance and maintenance issues. For instance, if a part of the process requires access to three database tables, it is both faster and clearer to allow the DBMS engine return a single query than compare the tables in the API code. Similarly every business rule coded in the client side reduces re-usability potential.
    Top quality developers understand these issues and can optimize their designs to take advantages of the strengths of the component technologies.
  5. The ability to stay current with new trends and technologies. Technology is constantly evolving, and a good software engineer or programmer should be able to stay up-to-date on the latest trends and technologies in order to be able to create the best possible products.

To conclude:

Below are other things to consider when hiring good software engineers or programmers:

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  1. The ability to write clean, well-organized code. This is a key indicator of a good software engineer or programmer. The ability to write code that is easy to read and understand is essential for creating high-quality software.
  2. The ability to test and debug code. A good coder should be able to test their code thoroughly and identify and fix any errors that may exist.
  3. The ability to write efficient code. Software engineering is all about creating efficient solutions to problems. A good software engineer or programmer will be able to write code that is efficient and effective.
  4. The ability to work well with others. Software engineering is typically a team-based effort. A good software engineer or programmer should be able to work well with others in order to create the best possible product.
  5. The ability to stay current with new trends and technologies.

What are the Greenest or Least Environmentally Friendly Programming Languages?

What are the Greenest or Least Environmentally Programming Languages?

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What are the Greenest or Least Environmentally Friendly Programming Languages?

Technology has revolutionized the way we live, work, and play. It has also had a profound impact on the world of programming languages. In recent years, there has been a growing trend towards green, energy-efficient languages such as C and C++.  C++ and Rust are two of the most popular languages in this category. Both are designed to be more efficient than traditional languages like Java and JavaScript. And both have been shown to be highly effective at reducing greenhouse gas emissions. So if you’re looking for a language that’s good for the environment, these two are definitely worth considering.

The study below runs 10 benchmark problems in 28 languages [1]. It measures the runtime, memory usage, and energy consumption of each language. The abstract of the paper is shown below.

“This paper presents a study of the runtime, memory usage and energy consumption of twenty seven well-known software languages. We monitor the performance of such languages using ten different programming problems, expressed in each of the languages. Our results show interesting findings, such as, slower/faster languages consuming less/more energy, and how memory usage influences energy consumption. We show how to use our results to provide software engineers support to decide which language to use when energy efficiency is a concern”. [2]

According to the “paper,” in this study, they monitored the performance of these languages using different programming problems for which they used different algorithms compiled by the “Computer Language Benchmarks Game” project, dedicated to implementing algorithms in different languages.

The team used Intel’s Running Average Power Limit (RAPL) tool to measure power consumption, which can provide very accurate power consumption estimates.

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The research shows that several factors influence energy consumption, as expected. The speed at which they are executed in the energy consumption is usually decisive, but not always the one that runs the fastest is the one that consumes the least energy as other factors enter into the power consumption equation besides speed, as the memory usage.

Energy

From this table, it is worth noting that C, C++and Java are among the languages that consume the least energy. On the other hand, JavaScript consumes almost twice as much as Java and four times what C consumes. As an interpreted language, Python needs more time to execute and is, therefore, one of the least “green” languages, occupying the position of those that consume the most energy.

What are the Greenest or Least Environmentally Friendly Programming Languages?
What are the Greenest or Least Environmentally Friendly Programming Languages?


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Time:

The results are similar to the energy expenditure; the faster a programming language is, the less energy it expends.

Greenest Programming Languages

Memory

In terms of memory consumption, we see how Java has become one of the most memory-consuming languages along with JavaScript.

Memory ranking.

Ranking

In this ranking, we can see the “greenest” and most efficient languages are: C, C+, Rust, and Java, although this last one shoots the memory usage.

From the Paper: Normalized global results for Energy, Time, and Memory.

What are the Greenest or Least Environmentally Friendly Programming Languages?

To conclude: 

Most Environmentally Friendly Languages: C, Rust, and C++
Least Environmentally Friendly Languages: Ruby, Python, Perl

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Although this study may seem curious and without much practical application, it may help design better and more efficient programming languages. Also, we can use this new parameter in our equation when choosing a programing language.

This parameter can no longer be ignored in the future or almost the present; besides, the fastest languages are generally also the most environmentally friendly.

If you’re interested in something that is both green and energy efficient, you might want to consider the Groeningen Programming Language (GPL). Developed by a team of researchers at the University of Groningen in the Netherlands, GPL is a relatively new language that is based on the C and C++ programming languages. Python and Rust are also used in its development. GPL is designed to be used for developing energy efficient applications. Its syntax is similar to other popular programming languages, so it should be relatively easy for experienced programmers to learn. And since it’s open source, you can download and use it for free. So why not give GPL a try? It just might be the perfect language for your next project.

Top 10 Caveats – Counter arguments:

#1 C++ will perform better than Python to solve some simple algorithmic problems. C++ is a fairly bare-bone language with a medium level of abstraction, while Python is a high-level languages that relies on many external components, some of which have actually been written in C++. And of course C++ will be efficient than C# to solve some basic problem. But let’s see what happens if you build a complete web application back-end in C++.

#2: This isn’t much useful. I can imagine that the fastest (performance-wise) programming languages are greenest, and vice versa. However, running time is not only the factor here. An engineer may spend 5 minutes writing a Python script that does the job pretty well, and spends hours on debugging C++ code that does the same thing. And the performance difference on the final code may not differ much!

#3:  Has anyone actually taken a look at the winning C and Rust solutions? Most of them are hand-written assembly code masked as SSE intrinsic. That is the kind of code that only a handful of people are able to maintain, not to mention come up with. On the other hand, the Python solutions are pure Python code without a trace of accelerated (read: written in Fortran, C, C++, and/or Rust) libraries like NumPy used in all sane Python projects.

#4:  I used C++ years ago and now use Python, for saving energy consumption, I turn off my laptop when I got off work, I don’t use extra monitors, my AC is always set to 28 Celsius degree, I plan to change my car to electrical one, and I use Python.

#5: I disagree. We should consider the energy saved by the products created in those languages. For example, a C# – based Microsoft Teams allows people to work remotely. How much CO2 do we save that way? 😉

Now, try to do the same in C.

#6 Also, some Python programs, such as anything using NumPy, spend a considerable fraction of their cycles outside the Python interpreter in a C or C++ library..

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I would love to see a scatterplot of execution time vs. energy usage as well. Given that modern CPUs can turbo and then go to a low-power state, a modest increase of energy usage during execution can pay dividends in letting the processor go to sleep quicker.

An application that vectorized heavily may end up having very high peak power and moderately higher energy usage that’s repaid by going to sleep much sooner. In the cell phone application processor business, we called that “race to sleep.” By Joe Zbiciak

#7  By Tim Mensch : It’s almost complete garbage.

If you look at the TypeScript numbers, they are more than 5x worse than JavaScript.

This has to mean they were running the TypeScript compiler every time they ran their benchmark. That’s not how TypeScript works. TypeScript should be identical to JavaScript. It is JavaScript once it’s running, after all.

Given that glaring mistake, the rest of their numbers are suspect.

I suspect Python and Ruby really are pretty bad given better written benchmarks I’ve seen, but given their testing issues, not as bad as they imply. Python at least has a “compile” phase as well, so if they were running a benchmark repeatedly, they were measuring the startup energy usage along with the actual energy usage, which may have swamped the benchmark itself.

PHP similarly has a compile step, but PHP may actually run that compile step every time a script is run. So of all of the benchmarks, it might be the closest.

I do wonder if they also compiled the C and C++ code as part of the benchmarks as well. C++ should be as optimized or more so than C, and as such should use the same or less power, unless you’re counting the compile phase. And if they’re also measuring the compile phase, then they are being intentionally deceptive. Or stupid. But I’ll go with deceptive to be polite. (You usually compile a program in C or C++ once and then you can run it millions or billions of times—or more. The energy cost of compiling is miniscule compared to the run time cost of almost any program.)

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I’ve read that 80% of all studies are garbage. This is one of those garbage studies.

#8 By Chaim Solomon: This is nonsense

This is nonsense as it runs low-level benchmarks that benchmark basic algorithms in high-level languages. You don’t do that for anything more than theoretical work.

Do a comparison of real-world tasks and you should find less of a spread.

Do a comparison of web-server work or something like that – I guess you may find a factor of maybe 5 or 10 – if it’s done right.

Don’t do low-level algorithms in a high-level language for anything more than teaching. If you need such an algorithm – the way to do it is to implement it in a library as a native module. And then it’s compiled to machine code and runs as fast as any other implementation.

#9 By Tim Mensch

It’s worse than nonsense. TypeScript complies directly to JavaScript, but gets a crazy worse rating somehow?!

#10 By Tim Mensch

For NumPy and machine learning applications, most of the calculations are going to be in C.

The world I’ve found myself in is server code, though. Servers that run 24/7/365.

And in that case, a server written in C or C++ will be able to saturate its network interface at a much lower continuous CPU load than a Python or Ruby server can. So in that respect, the latter languages’ performance issues really do make a difference in ongoing energy usage.

But as you point out, in mobile there could be an even greater difference due to the CPU being put to sleep or into a low power mode if it finishes its work more quickly.

 

What is the single most influential book every Programmers should read

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What is the single most influential book every Programmers should read

There are a lot of books that can be influential to programmers. But, what is the one book that every programmer should read? This is a question that has been asked by many, and it is still up for debate. However, there are some great contenders for this title. In this blog post, we will discuss three possible books that could be called the most influential book for programmers. So, what are you waiting for? Keep reading to find out more!

What is the single most influential book every Programmers should read
What is the single most influential book every Programmers should read

Source: Wikipedia

What is the single most influential book every Programmers should read
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Ok…I think this is one of the most important questions to answer. According to the my personal experience as a Programmer, I would say you must learn following 5 universal core concepts of programming to become a successful Java programmer.

(1) Mastering the fundamentals of Java programming Language – This is the most important skill that you must learn to become successful java programmer. You must master the fundamentals of the language, specially the areas like OOP, Collections, Generics, Concurrency, I/O, Stings, Exception handling, Inner Classes and JVM architecture.

Recommended readings are OCA Java SE 8 Programmer by by Kathy Sierra and Bert Bates (First read Head First Java if you are a new comer ) and Effective Java by Joshua Bloch.

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(2) Data Structures and Algorithms – Programming languages are basically just a tool to solve problems. Problems generally has data to process on to make some decisions and we have to build a procedure to solve that specific problem domain. In any real life complexity of the problem domain and the data we have to handle would be very large. That’s why it is essential to knowing basic data structures like Arrays, Linked Lists, Stacks, Queues, Trees, Heap, Dictionaries ,Hash Tables and Graphs and also basic algorithms like Searching, Sorting, Hashing, Graph algorithms, Greedy algorithms and Dynamic Programming.

Recommended readings are Data Structures & Algorithms in Java by Robert Lafore (Beginner) , Algorithms Robert Sedgewick (intermediate) and Introduction to Algorithms-MIT press by CLRS (Advanced).

(3) Design Patterns – Design patterns are general reusable solution to a commonly occurring problem within a given context in software design and they are absolutely crucial as hard core Java Programmer. If you don’t use design patterns you will write much more code, it will be buggy and hard to understand and refactor, not to mention untestable and they are really great way for communicating your intent very quickly with other programmers.


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Recommended readings are Head First Design Patterns Elisabeth Freeman and Kathy Sierra and Design Patterns: Elements of Reusable by Gang of four.

(4) Programming Best Practices – Programming is not only about learning and writing code. Code readability is a universal subject in the world of computer programming. It helps standardize products and help reduce future maintenance cost. Best practices helps you, as a programmer to think differently and improves problem solving attitude within you. A simple program can be written in many ways if given to multiple developers. Thus the need to best practices come into picture and every programmer must aware about these things.

Recommended readings are Clean Code by Robert Cecil Martin and Code Complete by Steve McConnell.

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(5) Testing and Debugging (T&D) – As you know about the writing the code for specific problem domain, you have to learn how to test that code snippet and debug it when it is needed. Some programmers skip their unit testing or other testing methodology part and leave it to QA guys. That will lead to delivering 80% bugs hiding in your code to the QA team and reduce the productivity and risking and pushing your project boundaries to failure. When a miss behavior or bug occurred within your code when the testing phase. It is essential to know about the debugging techniques to identify that bug and its root cause.

Recommended readings are Debugging by David Agans and A Friendly Introduction to Software Testing by Bill Laboon.

I hope these instructions will help you to become a successful Java Programmer. Here i am explain only the universal core concepts that you must learn as successful programmer. I am not mentioning any technologies that Java programmer must know such as Spring, Hibernate, Micro-Servicers and Build tools, because that can be change according to the problem domain or environment that you are currently working on…..Happy Coding!

Summary: There’s no doubt that books have had a profound influence on society and the advancement of human knowledge. But which book is the most influential for programmers? Some might say it’s The Art of Computer Programming, or The Pragmatic Programmer. But I would argue that the most influential book for programmers is CODE: The Hidden Language of Computer Hardware and Software. In CODE, author Charles Petzold takes you on a journey from the basics of computer hardware to the intricate workings of software. Along the way, you learn how to write code in Assembly language, and gain an understanding of how computers work at a fundamental level. If you’re serious about becoming a programmer, then CODE should be at the top of your reading list!

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