Category: Software Engineering

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The Role of Software in Industrial Motor Applications

A row of blue electric motors mounted on a metal platform surrounded by pipes, valves, and industrial equipment.

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Job TitleStatusPay
Full-Stack Engineer Strong match, Full-time $150K - $220K / year
Developer Experience and Productivity Engineer Pre-qualified, Full-time $160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract) Contract $90 / hour
DevOps Engineer (India) Full-time $20K - $50K / year
Senior Full-Stack Engineer Full-time $2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India Contract $20 - $30 / hour
Senior Software Engineer Contract $100 - $200 / hour
Senior Software Engineer Pre-qualified, Full-time $150K - $300K / year
Senior Full-Stack Engineer: Latin America Full-time $1.6K - $2.1K / week
Software Engineering Expert Contract $50 - $150 / hour
Generalist Video Annotators Contract $45 / hour
Generalist Writing Expert Contract $45 / hour
Editors, Fact Checkers, & Data Quality Reviewers Contract $50 - $60 / hour
Multilingual Expert Contract $54 / hour
Mathematics Expert (PhD) Contract $60 - $80 / hour
Software Engineer - India Contract $20 - $45 / hour
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Designers Contract $50 - $70 / hour
Chemistry Expert (PhD) Contract $60 - $80 / hour

Industrial motor systems power nearly every sector, from manufacturing to water treatment. Motors alone no longer carry the load—software now plays an increasingly important role in running those systems smarter and more efficiently.

Engineers, technicians, and systems integrators depend on precise software tools to control, monitor, and adapt motor behavior. Understanding how software influences motor performance reveals new opportunities for innovation, reliability, and long-term savings.

Motor Control Through Intelligent Software Systems

Modern control systems rely on intelligent software to manage motor speed, direction, and torque with real-time accuracy. Platforms like variable frequency drives and programmable logic controllers continuously adjust motor output based on real-world data. Engineers configure digital parameters to match specific operational demands and improve response time.

Operators gain flexibility by modifying settings remotely or building custom control sequences. Technicians can automate entire systems, from conveyor belts to robotic arms, without replacing physical components. One key benefit that highlights the role of software in industrial motor applications lies in its ability to control performance dynamically without interrupting workflow.

Predictive Maintenance and System Longevity

Software platforms monitor motor systems around the clock, collecting data from temperature, vibration, and power sensors. Engineers analyze this information to predict when a component will fail, not just react after a breakdown. Maintenance teams receive alerts before issues escalate into expensive damage.

Artificial intelligence and machine learning help refine those predictions over time. Advancements in sensors, AI tools, and software help companies with important preventative maintenance of water pumps, generators, and more, while extending asset life cycles across industrial environments.

Integration With Industrial IoT and Cloud Platforms

Software connects motors to broader digital ecosystems through industrial IoT frameworks. Systems collect real-time operational data and upload it to secure cloud platforms where managers track performance from anywhere. Teams use dashboards to identify inefficiencies, tweak motor behavior, or schedule service with precision.

Remote access also improves consistency across multi-site operations. Engineers control different motor systems from a single interface without losing visibility. Digital ecosystems improve collaboration between IT teams and facility managers through shared access and up-to-date system data.

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Efficiency Optimization and Energy Savings

Software plays a direct role in cutting motor energy consumption by reducing idle time and adjusting output to meet demand. Smart load balancing reduces peaks and keeps motors running in optimal efficiency zones. Control systems fine-tune motor parameters automatically during shifting production requirements.

The growing role of software in industrial motor applications focuses on optimization and efficiency. Facility managers use analytics tools to measure savings and track improvements in real time.


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Energy dashboards reveal trends across shifts, product runs, or seasonal demand. Companies that invest in smarter control software gain both financial and operational returns. That fact alone reflects

Safety Features and Operational Protection

Software adds built-in safety features that shut motors down instantly during fault conditions. Speed limits, torque restrictions, and electronic guards protect both equipment and personnel during critical operations. Engineers can preprogram emergency responses to mechanical failure, overload, or electrical surges.

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Customization and Adaptive Motor Programming

Motor systems often require unique configurations that off-the-shelf hardware can’t handle. Engineers use adaptive software to customize acceleration rates, directional sequences, or multi-motor synchronization. Operators apply updates without swapping parts or overhauling system architecture.

Software evolves as operational needs change. Manufacturers introduce firmware upgrades, new logic modules, or plug-in analytics tools that scale with production. Adaptive control programs give engineers the freedom to innovate while maintaining stability and uptime.

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Top 10 ways for software engineers or developers to gain more power in their companies?

Top 10 ways for software engineers or developers to gain more power in their companies?

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

Job TitleStatusPay
Full-Stack Engineer Strong match, Full-time $150K - $220K / year
Developer Experience and Productivity Engineer Pre-qualified, Full-time $160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract) Contract $90 / hour
DevOps Engineer (India) Full-time $20K - $50K / year
Senior Full-Stack Engineer Full-time $2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India Contract $20 - $30 / hour
Senior Software Engineer Contract $100 - $200 / hour
Senior Software Engineer Pre-qualified, Full-time $150K - $300K / year
Senior Full-Stack Engineer: Latin America Full-time $1.6K - $2.1K / week
Software Engineering Expert Contract $50 - $150 / hour
Generalist Video Annotators Contract $45 / hour
Generalist Writing Expert Contract $45 / hour
Editors, Fact Checkers, & Data Quality Reviewers Contract $50 - $60 / hour
Multilingual Expert Contract $54 / hour
Mathematics Expert (PhD) Contract $60 - $80 / hour
Software Engineer - India Contract $20 - $45 / hour
Physics Expert (PhD) Contract $60 - $80 / hour
Finance Expert Contract $150 / hour
Designers Contract $50 - $70 / hour
Chemistry Expert (PhD) Contract $60 - $80 / hour

Top 10 ways for software engineers or developers to gain more power in their companies?

Power is a relative term. In business, power is the ability to influence others to achieve a desired outcome. As a software engineer, you may not be the CEO of your company, but that doesn’t mean you can’t wield a considerable amount of power. Here are the top 10 ways for software engineers to gain more power in their companies.

1. Become an expert in your field.

The first way to gain more power in your company is to become an expert in your field. When you know more than anyone else in the room, people are going to listen to what you have to say. Be the go-to person for questions about your area of expertise and make yourself indispensable.

2. Make friends with the right people.

It’s not what you know, it’s who you know. Making friends with the right people can open doors and help you get your foot in the door. If you want to gain more power in your company, start by making friends with the people who already have it.

3. Speak up and be heard.

If you want to influence others, you need to be vocal about it. Don’t be afraid to speak up and share your ideas with others. The more you speak up, the more likely it is that your voice will be heard and that you’ll be able to make a difference.

4. Network, network, network!

Get out there and meet people! The more connections you have, the greater your chances of success will be. Go to industry events, meetups, and conferences and make yourself known. The more visible you are, the more likely it is that you’ll be able to exert your influence when it matters most.

5. Write articles or blog posts about your area of expertise.

Sharing your knowledge with others is a great way to build credibility and gain recognition as an expert in your field. Writing articles or blog posts about your area of expertise is a great way to get started. Not only will you get exposure for your ideas, but you’ll also get feedback from others that can help improve your thinking. And, if people like what they read, they may even start quoting you as an expert!

6. Stay up-to-date on industry trends.

If you want to be seen as an expert, it’s important that you stay up-to-date on industry trends. Keep abreast of new developments in your field so that you can provide valuable insights when opportunities or problems arise. Staying current will also give you an edge over competition when it comes time for promotions or raises!

7. Give presentations or talks about your area of expertise .

Sharing your knowledge with others is a great way to build credibility and gain recognition as an expert in your field . If there are conferences or meetups related to your industry , see if there are any speaking opportunities available . Giving presentations or talks is also a great opportunity t o network with other professionals in your field . 8\. Volunteer t o work on high – profile projects . If y ou want t o increase y our visibility within y our company , volunteering t o work on high – profile projects is a great way t o do it . Not only will y ou get t o work on some challenging and interesting projects , but y ou’ll also get t o rub shoulders with some influential people . And , if y o u do a good job , y our work will speak for itself — which can only improve y our standing within the company .

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9. Seek out mentorship opportunities.

A mentor can provide invaluable guidance — both professional and personal — as you navigate your career. Mentors can help you develop both professionally and personally, and they can also introduce you to their networks . If you’re interested in finding a mentor, seek out programs within your company or look for individuals who have already achieved what you aspire to accomplish .

10. Be assertive

— but not aggressive . Being assertive means being confident enough in your abilities to speak up for yourself when necessary , but not being so aggressive that your actions alienate others. When you’re assertive, people are more likely to listen to what you have to say because they respect  your confidence in yourself. But being too aggressive can create hostility which can shake people’s confidence in you.


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Top 10 ways for software engineers or developers to gain more power in their companies?
software engineer to ceo

According to Kurt Guntheroth, software developers can gain more power in their companies doing the following:

  • Working for the same company for many years gives you more power. The opinions of recent hires are always distrusted, no matter how smart they are. Sorry, that’s not a thing you can do now.
  • Being among the first developers at a startup gives you a lot of power, because you know how everything works. The guys who come in a year later will never know as much as you do.
  • Becoming good friends and golf/squash/kayaking buddies with the lead or manager gives you a ton of power. Not all managers are social enough to make that work, though. I’ve known several rather mediocre engineers who wielded power way beyond what they should have because they had been around awhile, and were friends with the boss.
  • A history of calling the outcomes of decisions correctly makes you slightly more powerful. The trick comes in balancing how much to advertise your correct calls. Too much and you sound snooty. Not enough and being right doesn’t even matter.
  • Willingness to write spec documents gives you a lot of power over a design. It’s my experience that only about half of the excellent software developers I have ever worked with could string two grammatical sentences together.

Are you a software engineer or developer who feels like you don’t have enough power or sway in your company? If so, don’t worry – you’re not alone. The truth is, there are often a lot of people in companies who feel like they don’t have enough power.

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

But the good news is that there are things you can do to change that. In this blog post, we’ll give you 10 actionable tips that you can use to gain more power in your company.

1. Get involved in decision-making processes.
2. Speak up when you have an idea or perspective to share.
3. Become a mentor or coach to others.
4. Be a thought leader by writing blog posts or articles, giving talks, or teaching classes.
5. Develop relationships with people in other departments or companies.
6. Join or create employee resource groups.
7. Serve on committees or working groups.
8. Volunteer for special projects.
9. Network outside of work hours.
10. Make sure your performance review focuses on your accomplishments.

11. Become a subject matter expert.
12. Develop a strong understanding of the business.
13. Get involved in strategic decision-making.
14. Foster relationships with key stakeholders.
15. Drive change within your team.
16. Improve process and efficiency within your department.
17. Champion new initiatives and ideas.
18. Invest in yourself and your career development.
19. Be an active participant in industry thought leadership.
20. Leverage your technical expertise to improve customer experiences or solve business problems.

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If you’re a software engineer or developer who feels like you could have more power in your company, we hope this blog post was helpful for you! Remember, gaining more power in your company is all about taking action and putting yourself out there – so don’t be afraid to get started today with some of the tips we listed above.”

These are just a few ways that software engineers can become more powerful within their companies. By becoming a subject matter expert, developing a strong understanding of the business, and getting involved in strategic decision-making, you can position yourself as a key player in your organization. So take action today and start making your voice heard!

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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?

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

Job TitleStatusPay
Full-Stack Engineer Strong match, Full-time $150K - $220K / year
Developer Experience and Productivity Engineer Pre-qualified, Full-time $160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract) Contract $90 / hour
DevOps Engineer (India) Full-time $20K - $50K / year
Senior Full-Stack Engineer Full-time $2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India Contract $20 - $30 / hour
Senior Software Engineer Contract $100 - $200 / hour
Senior Software Engineer Pre-qualified, Full-time $150K - $300K / year
Senior Full-Stack Engineer: Latin America Full-time $1.6K - $2.1K / week
Software Engineering Expert Contract $50 - $150 / hour
Generalist Video Annotators Contract $45 / hour
Generalist Writing Expert Contract $45 / hour
Editors, Fact Checkers, & Data Quality Reviewers Contract $50 - $60 / hour
Multilingual Expert Contract $54 / hour
Mathematics Expert (PhD) Contract $60 - $80 / hour
Software Engineer - India Contract $20 - $45 / hour
Physics Expert (PhD) Contract $60 - $80 / hour
Finance Expert Contract $150 / hour
Designers Contract $50 - $70 / hour
Chemistry Expert (PhD) Contract $60 - $80 / hour

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.

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:


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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:

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.

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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.

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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.


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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.

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

Why can’t a macOS be installed in a Windows computer?

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

Job TitleStatusPay
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Developer Experience and Productivity Engineer Pre-qualified, Full-time $160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract) Contract $90 / hour
DevOps Engineer (India) Full-time $20K - $50K / year
Senior Full-Stack Engineer Full-time $2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India Contract $20 - $30 / hour
Senior Software Engineer Contract $100 - $200 / hour
Senior Software Engineer Pre-qualified, Full-time $150K - $300K / year
Senior Full-Stack Engineer: Latin America Full-time $1.6K - $2.1K / week
Software Engineering Expert Contract $50 - $150 / hour
Generalist Video Annotators Contract $45 / hour
Generalist Writing Expert Contract $45 / hour
Editors, Fact Checkers, & Data Quality Reviewers Contract $50 - $60 / hour
Multilingual Expert Contract $54 / hour
Mathematics Expert (PhD) Contract $60 - $80 / hour
Software Engineer - India Contract $20 - $45 / hour
Physics Expert (PhD) Contract $60 - $80 / hour
Finance Expert Contract $150 / hour
Designers Contract $50 - $70 / hour
Chemistry Expert (PhD) Contract $60 - $80 / hour

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

Computer science and software engineering are fascinating fields that continue to evolve and surprise us. Computer science and software engineering are disciplines that are essential for the modern world. They have led to the development of many innovative products and services that have made our lives easier and more efficient. In this blog post, we’ll explore the top 10 wonders of computer science and software engineering.

The things Alan Keys  found to be astonishing and amazing (and shocking) are:

  1. Turing’s notion of machines that can simulate machines completely by interpreting their descriptions (exhibiting the programmable computer as “a language machine” and a “meta-language machine” — along with this is the simplicity of what is required to do so (a great book is Marvin Minsky’s “Computation: Finite and Infinite Machines”). Turing’s approach is much more of a “real CS” approach compared to Goedel’s earlier methods, and soon led to a large number of important next steps.
  2. How simple (a) it is to design a whole computer from just one kind of logical element (e.g. “NOT-BOTH”), especially when compared (b) to how Russell and Whitehead struggled to “bootstrap mathematics, etc., from logic at the turn of the last century. (This is one of those “Point of View is Worth 80 IQ Points” …)
  3. Lisp, and McCarthy’s general approach to “mathematical theories of computation” and having languages that can act as their own metalanguage. One of the great cornucopias of our field.
  4. Sketchpad by Ivan Sutherland for so many reasons, including: the approach to interactive computer graphics and the simulations of the graphic relationships, the “object-oriented” approach to definition and deriving new kinds of things (including “masters” and making instances from masters), enormous virtual worlds that are windowed on the display, the use of goal-directed programming with the system solving the simultaneous goals in real-time, etc. And more, including the demonstration that a simulated computer on a computer need look nothing like the underlying hardware or any “normal” idea of “computer”.
  5. The big Shannon et al. ideas about how to have imperfect things be organized in systems that are much more perfectly behaved even if the organizational mechanisms are themselves noisy. Includes all forms of “noise”, “representations”, “communications”, “machines”, etc. and poking deeply into Biology and how living things work. Nice implications for “stochastic computing” of many kinds which are needed more and more as things scale.
  6. The deep implications of “symbolic computation” (now a very un-funded area) for being able to move from the trivialities of “data” (no matter how voluminous”) to the profundities and powers of “Meaning”. This used to be called “AI” and now has to be called “real AI” or “strong AI” (it would be much better under a less loaded term: how about “Flexible Competence”?)
  7. The Internet. Certainly the best thing done by my research community, and the first real essay into the kinds of scaling and stabilities that all computer science should be trying to understand and improve. This was a great invention and development process in all ways, and — by looking at Biology, which inspired but we really couldn’t use — it had a reasonable chance to work. That it was able to scale stably over more than 10 (maybe 11) orders of magnitude, as indeed planned, is still kind of amazing to me (even though it should have). Judging from most software systems today not being organized like the Internet, one is forced into the opinion that most computerists don’t understand it, why it is great (and maybe don’t even think of it as the fruits of “real computer science” because it just works so much better and more reliably than most other attempted artifacts in the field).
  8. Application: #1: Self-Driving Cars
    Self-driving cars are one of the most hyped technologies of the past few years. And for good reason! These autonomous vehicles have the potential to drastically reduce accidents and improve traffic flow. While there are still some kinks to be ironed out, it’s only a matter of time until self-driving cars become the norm.
  9. Application: #2: Artificial Intelligence
    Artificial intelligence is another technology that is rapidly evolving. AI is being used in a variety of ways, from personal assistants like Siri to chatbots that can carry on a conversation. As AI gets more sophisticated, its capabilities will only continue to grow.
  10. Application: #3: Virtual Reality
    Virtual reality is another exciting technology with a lot of potential. VR has already been used in a number of different industries, from gaming to medicine. And as VR technology gets more advanced, we can only imagine the new and innovative ways it will be used in the future.
  11. Application: #4: Blockchain
    You’ve probably heard of Bitcoin, the digital currency that uses blockchain technology. But what exactly is blockchain? In short, it’s a decentralized database that can be used to store data securely. Blockchain is already being used in a number of different industries, and its applications are only growing.
  12. Application: #5: Internet of Things
    The internet of things refers to the growing trend of interconnected devices. From your phone to your fridge, more and more devices are being connected to the internet. This allows them to share data and makes them easier to control. The internet of things is changing the way we live and work, and there’s no doubt that its impact will only continue to grow in the years to come.
  13. Application: #6: Data Science
    Data science is a relatively new field that combines statistics, computer science, and domain expertise to extract knowledge from data. Data science is being used in a variety of industries, from healthcare to retail. And as data becomes increasingly abundant, data scientists will become even more important in helping organizations make sense of it all.
  14. Application: #7: Machine Learning
    Machine learning is a subset of artificial intelligence that allows computers to learn from data without being explicitly programmed. Machine learning is being used in a number of different ways, from fraud detection to object recognition. As machine learning algorithms get more sophisticated, they will continue to revolutionize the way we live and work.
  15. Application: #8 Cybersecurity : Cybersecurity is a critical concern for businesses and individuals alike. With so much of our lives taking place online, it’s important to have measures in place to protect our information from hackers and cyber criminals.

These are just some of the many wonders of computer science and software engineering! Each one has the potential to change our world in amazing ways. We can’t wait to see what else these fields have in store for us!

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

Other notable wonders of computing:

#16 Mobile phones are handheld devices that allow us to make calls, send texts, and access the internet while on the go. They have become an indispensable part of our lives and have transformed the way we stay connected with others.

#17 Social Media
Social media platforms like Facebook, Twitter, and Instagram have changed the way we interact with each other. They provide us with a space to share our thoughts, feelings, and experiences with friends and family members who might be located anywhere in the world.

#18 Cloud Computing
Cloud computing is a model of computing that allows users to access data and applications over the internet. It has made it possible for businesses to operate more efficiently by reducing their reliance on physical infrastructure.

#19 Big Data
Big data refers to large data sets that can be analyzed to reveal patterns and trends. It is being used by businesses in a variety of industries to make better decisions about everything from product development to marketing strategies.

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#20 Augmented Reality Augmented reality is a type of technology that overlays digital information on real-world objects. It has many potential applications, including education, gaming, and navigation.

#21 3D Printing 3D printing is a process of creating three-dimensional objects from a digital file. It has revolutionized manufacturing by making it possible to create customized products quickly and easily. These are just some of the things that computer science and software engineering have made possible! As you can see, these disciplines have had a major impact on our world and will continue to shape the future as we move into the digital age.


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Conclusion: So there you have it! These are the top 10 wonders of computer science and software engineering according to me. Do you agree with my list? What would you add or remove? Let me know in the comments below!

Addendum:

In the spirit of choosing artifacts over ideas, I would replace “symbolic computation “ with Unix.

I’ve mentioned elsewhere that one can praise in a sentence or two, but criticism ethically demands more careful substantiation.

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

All I’ll say here is that when Unix was being invented Xerox Parc was already successfully making systems with dynamic objects that required no separate OS layering or system builds. That said, Doug McIlroy did find the start of what could have been really fruitful ideas when he implemented “pipes” programming. If they had seen what could have been done if they had reduced the process overhead to zero bytes, and gone to a dynamic language, then something great could have resulted. By Alan Kay

What do you mean by organizing software systems like the internet?

Just to get you started: consider that the Internet’s (a) processes do not have to be stopped to fix, change, add to etc. (b) messages are not commands (c) units of computation are perfectly encapsulated (only the interior code code of a computer can decide to do anything or nothing) (d) units of transmission can be very badly damaged and messages will still get through, (e) scaling is more than 10 orders of magnitude (f) and on and on and on.

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What SW systems to you know of that are remotely like this (that don’t depend intrinsically on what is wonderful about the Internet)?

This doesn’t mean the Internet is a perfect design at all. For example, the add-on of DNS was not nearly as good and lasting a scheme as the base semantics of TCP/IP. (It’s crazy that there are not unique IDs for every kind of entity manifested within the Internet system. Bob Kahn has been advocating this for several decades now — Joe Armstrong among others has pointed out that the modern hashing schemes (SHA256, etc.) are good enough to provide excellent unique IDs for Internet entities, etc.)

But the Internet did completely raise many bars qualitatively and astonishingly higher. It should be used as a starting point in thinking about how most SW systems can and should be organized.

Just a note about this big shift in thinking within the ARPA/Parc community — it is hard to pin down just when. But Wes Clark used to say that no computer is any good if it can’t work perfectly with 10% of its wires disconnected! Packet Switching (American version at RAND ARPA project in early 60s by Paul Baran) meant that you could do store and forward with a number of routes. If you made the protocol full-duplex, you could guarantee *eventually perfect* delivery of packets. At Parc the huge shift from “theoretical” to “punch in the face reality” came as we realized just how wonderfully well the extremely simple Ethernet was actually performing. This led to the articulated idea that no computation should ever require having to be stopped in order to improve/change/etc it.

In other words, make the systems design such that things will work “eventually as wished”, so you can spend your brain cells on (a) better designs, and (b) better optimizations. The Internet grew out of the whole community’s strong and emotional realizations around these ideas. By Alan Kay


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Gemini, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)
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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

AI Jobs and Career

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Job TitleStatusPay
Full-Stack Engineer Strong match, Full-time $150K - $220K / year
Developer Experience and Productivity Engineer Pre-qualified, Full-time $160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract) Contract $90 / hour
DevOps Engineer (India) Full-time $20K - $50K / year
Senior Full-Stack Engineer Full-time $2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India Contract $20 - $30 / hour
Senior Software Engineer Contract $100 - $200 / hour
Senior Software Engineer Pre-qualified, Full-time $150K - $300K / year
Senior Full-Stack Engineer: Latin America Full-time $1.6K - $2.1K / week
Software Engineering Expert Contract $50 - $150 / hour
Generalist Video Annotators Contract $45 / hour
Generalist Writing Expert Contract $45 / hour
Editors, Fact Checkers, & Data Quality Reviewers Contract $50 - $60 / hour
Multilingual Expert Contract $54 / hour
Mathematics Expert (PhD) Contract $60 - $80 / hour
Software Engineer - India Contract $20 - $45 / hour
Physics Expert (PhD) Contract $60 - $80 / hour
Finance Expert Contract $150 / hour
Designers Contract $50 - $70 / hour
Chemistry Expert (PhD) Contract $60 - $80 / hour

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?

Ian Drake
  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:

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

Top Programming Books

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

Job TitleStatusPay
Full-Stack Engineer Strong match, Full-time $150K - $220K / year
Developer Experience and Productivity Engineer Pre-qualified, Full-time $160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract) Contract $90 / hour
DevOps Engineer (India) Full-time $20K - $50K / year
Senior Full-Stack Engineer Full-time $2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India Contract $20 - $30 / hour
Senior Software Engineer Contract $100 - $200 / hour
Senior Software Engineer Pre-qualified, Full-time $150K - $300K / year
Senior Full-Stack Engineer: Latin America Full-time $1.6K - $2.1K / week
Software Engineering Expert Contract $50 - $150 / hour
Generalist Video Annotators Contract $45 / hour
Generalist Writing Expert Contract $45 / hour
Editors, Fact Checkers, & Data Quality Reviewers Contract $50 - $60 / hour
Multilingual Expert Contract $54 / hour
Mathematics Expert (PhD) Contract $60 - $80 / hour
Software Engineer - India Contract $20 - $45 / hour
Physics Expert (PhD) Contract $60 - $80 / hour
Finance Expert Contract $150 / hour
Designers Contract $50 - $70 / hour
Chemistry Expert (PhD) Contract $60 - $80 / hour

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
Popular Programming Languages

What are the concepts every Java C# C++ Python Rust programmer must know?

 

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.

(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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Whether you're seeking roles in machine learning, data science, or other cutting-edge AI fields, Mercor offers a streamlined path to your ideal position. Explore the possibilities and accelerate your AI career by visiting Mercor through our exclusive referral link:

Find Your AI Dream Job on Mercor

Your next big opportunity in AI could be just a click away!

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.


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

(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.

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

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!

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By Aravinda Meewalaarachchi

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!

Programming Breaking News

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What are popular hobbies among Software Engineers?

Software Engineers Hobbies

AI Jobs and Career

And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.

Job TitleStatusPay
Full-Stack Engineer Strong match, Full-time $150K - $220K / year
Developer Experience and Productivity Engineer Pre-qualified, Full-time $160K - $300K / year
Software Engineer - Tooling & AI Workflows (Contract) Contract $90 / hour
DevOps Engineer (India) Full-time $20K - $50K / year
Senior Full-Stack Engineer Full-time $2.8K - $4K / week
Enterprise IT & Cloud Domain Expert - India Contract $20 - $30 / hour
Senior Software Engineer Contract $100 - $200 / hour
Senior Software Engineer Pre-qualified, Full-time $150K - $300K / year
Senior Full-Stack Engineer: Latin America Full-time $1.6K - $2.1K / week
Software Engineering Expert Contract $50 - $150 / hour
Generalist Video Annotators Contract $45 / hour
Generalist Writing Expert Contract $45 / hour
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What are popular hobbies among Software Engineers?

Just like any other profession, Software Engineers have their own unique set of hobbies that they enjoy. While some programmers prefer to relax by playing video games or watching TV, others like to get more involved in their hobbies and spend their free time learning new things or being creative. If you’re curious about what some of the most popular hobbies among software engineers are, read on!

1. Reading
2. Playing video games
3. Working on personal projects
4. Cooking
5. Hiking/running
6. Watching TV/movies

 

Stereotypically, video games and board games are supposed to be popular, but of the many programmers I know, I can think of only two that are really into games enough to call it a hobby rather than (as they are for most of us) a casual diversion. One is more board-gamery, and the other is more videogamer-uh-erery.

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

Outside of that, hobbies I know of amongst programmers I know (and I include myself) are:

  • Photography

  • Playing musical instruments and/or music production

  • Hiking

  • Bicycling

  • Motorcycling

  • Cars (motorsport, restoration, etc.)

  • 4x4s and off-roading

  • Electronics

  • Retrocomputing

  • Software side projects

  • Pets, usually dogs & cats but sometimes exotics

  • Do-it-yourself plumbing/electrical/carpentry/plastering/concrete

  • Cooking and/or foodie pursuits like visiting restaurants or inventing cocktails

  • Coffee (machines, roasting, blending, drinking)

  • Lego™

  • Card games (often poker or blackjack)

  • Rock climbing

  • Fishing

  • Target shooting

  • Archery

  • Kayaking

  • Painting, drawing or sculpture

  • Flying model airplanes, helicopters and/or rockets

  • Golf, football, tennis, raquetball, squash (spectatorship and/or playing)

  • Skiing

  • Chess

  • Soccer/Football

  • Martial Arts

  • Blogging

Summary:

How do Software Engineers spend their free time? What are their hobbies? Well, according to a recent survey, the most popular hobby among Software Engineers is programming. But that’s not all – they also enjoy playing video games, reading books, and spending time with friends and family. So what does this tell us about Software Engineers? Well, for one, they’re passionate about technology and love to stay up-to-date on the latest innovations. But they’re also well-rounded individuals who enjoy a variety of activities outside of work. So if you’re looking for someone to chat with about the latest tech trends or just want to find someone to play video games with, then a Software Engineer is your best bet!

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Source: What are popular hobbies among Software Engineers on Quora

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Will software engineers ever stop being in demand?

Will Software Engineer ever stop being in demand?

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Will software engineers ever stop being in demand?

It’s safe to say that software engineering is a hot profession. And it doesn’t look like demand for software engineers is going to decrease any time soon. So, if you’re thinking about becoming a software engineer, now is a good time to do it! But what does the future hold for this in-demand profession? Keep reading to find out!

What is a Software Engineer? According to Wikipedia, A software engineer is a person who applies the principles of software engineering to design, develop, maintain, test, and evaluate computer software. The term programmer is sometimes used as a synonym, but may also lack connotations of engineering education or skills.

It’s hard to believe, but demand for software engineers shows no sign of abating. Despite automation and the ever-growing availability of open source software, companies are still finding that they need people with specialized skills to design, build, and maintain their applications. 

The question is “Will software engineers ever stop being in demand?”

There are two schools of thought.

  • Those with a background in business see developers as commodities and fully believe that programmers will program themselves out of a job field. The idea is that in some distant future, jobs like project manager, product manager, and marketing manager will still be critical but programmers themselves will be extinct as a result of the tools they created.
  • The other school of thought is hard to understand because the programmers are laughing so hard they can’t talk.

Its funny that the marketing manager doesn’t think he’s going to get automated out of existence.

What’s interesting is that marketing and software engineers (product people) are inextricably tied and both respond to the ever-changing needs of human beings.

Finance, Accounting and Operational functions will increasingly be automated. Marketing can automate some things but for the foreseeable future, you will need a human being to use a mix of intuition and data to figure out what to build, just as you will need a human being to figure out the best way to build it (engineers).

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Any business partner worth their salt in today’s economy knows software engineers are your most valuable asset. Period.

“Any business partner worth their salt in today’s economy knows software engineers are your most valuable asset. Period.” I’m shocked and thrilled by this statement. I’ve often seen engineers treated as as expensive data entry specialists. But engineering has a steep learning curve and it requires a lot of time and concentration to master. Plus engineers acquire a lot of tribal knowledge during their time at a company, which is important because reading code is not as easy as reading a book. New engineers take time to get fully up to speed.


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Will software engineers ever stop being in demand?
Will software engineers ever stop being in demand?

In 1980 I read in a newspaper that software developers would be extinct by year 2000. Probably something that could be extrapolated from the Maya calendar or Nostradamus or something.

People have been predicting the demise of programmers since the invention of COBOL. That’s a long time ago, for you youngsters.

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Business people are taught to believe everyone but themselves are expendable. What we often ignore is that making people expendable requires code… the very people we wish not to be dependent on.

I wish business and engineering folks had a more symbiotic relationship, because the truth is, when business and engineers are on the same page and have mutual respect for the value the other brings, companies become unstoppable.

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I stopped laughing awhile ago, because the joke is so old that it stopped being funny a long time ago. I personally saw several iterations of software companies coming up with programmer replacement tools, and many bean counters actually bought into it and spent $billions. One can go through the glossary of dead buried developer replacement tools and take your pick, but most laymen probably heard of program generators and 4GL. None of them even came close to achieving their stated goal, and all are 6-feet under now. The most laughable part is that COBOL, the original human understandable programming language is still being used today. So, I say to those “visionaries” not to bother, but just keep using COBOL if replacing the programmers is your inspiration.

I heard this exact same claim 25 years ago from 4 Harvard grads (who were hiring myself and three of my colleagues to develop a product) who said XML would making coding obsolete.

I would love to watch a product manager attempt to use a tool like co-pilot to write code himself…on any sizable codebase, it would take ten developers ten years each to unravel the mess that individual would make per 10k lines of code he “produced”.

Take a hard look at Elon Musk
Not only can he code
He can imagine new modes of transportation
He can imagine new ways to recover rocket boosters
He can imagine new applications to compensate people for their labors
In Conclusion:
Egomaniacal Software Engineers need to be exited from the profession
Software Engineers who can use their coding and enterprise-architect skillsets to address a new market…are worth their weight in GOLD

Being a programmer who has tried “low code solutions” they are nothing like custom-made solutions by a developer. And even with low-code solutions, you still need to employ a lot of the same type of reasoning and skills that a programmer would or you’re going to come out with a crappy end result for all but the simplest automations. Even if it were the case that people think programmers will automate themselves out of a job (not something I foresee and if you understand programming, you see it too) but even if they would, this is VERY far in the future and not something I’ll be worrying about at night 🙂


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Alternate view:

I’m a software developer. However, I disagree with this. When artificial general intelligence arrives, it could in principle take the role of the engineer away from a human.

I’m aware of the comic strip jokes about this, but I feel they miss an important point. I’m not talking about there being an even higher level language that somehow simplifies the source code beyond what would be possible. You can make it as simple as possible, but not any simpler.

Consider a freelance engineer. Their job is to listen to the needs to the client, and fill in the gaps in their vague and informal specification, and to understand the clients needs in a way the client themselves cannot, to facilitate the development of a formal specification (the code itself), and its technical implementation.

The engineer chooses appropriate algorithms, researches appropriate technologies, and instruments the production of the software on behalf of their client.

I can’t see a reason why a generally intelligent machine could not fulfil the same role – we ourselves are probably just quasi-generally intelligent automatons. In this sense, the “even higher level language” is the system of inference the AI is using to be able to produce the complete and formal specification.

Of course, without proper AI, you can’t do this sort of thing properly. However, I think it’s a mistake to discount the possibility.

Probably, this is a very long way off into the future. There will surely still be people writing code for the fun of it. However, once the responsibility of a developer can be delegated to an AI, it makes for a good business case to use it. Possibly, the logical end of this technology is a self-sustaining system of machines that mine material, assemble factories, construct tools, and produce products – while repairing each other in the case of failures.

So keep on laughing for now, but probably you won’t have the last laugh. Hopefully, maybe idealistically, the last laugh will be shared between us all.

Rebuke:

What you’re missing is that we don’t even know what we don’t know about AGI yet. We’re barely closer to AGI than we’ve been for the past fifty years.

There’s another thing too: Programming is one of the harder human endeavors. The very first AGI may not even be much more capable than an average human with an integrated mind reading calculator. There’s no guarantee that it will immediately be smarter than a human. But it would be able to do menial jobs that still need a human, assuming its hardware can be made to be cheap enough. (Say it requires some new kind of chip with quantum properties.)

As it learns more it will be able to do harder and harder jobs.

So effectively every other job will be able to be done by an AGI before an AGI will be able to do the job of a human.

Meaning that if cheap AGI becomes a thing, humans will either be killed off a la Terminator or put on some kind of UBI well before software engineers lose their jobs to computers. If AGI stays perpetually expensive, though, then you’ll just have new AGI “people” joining the workforce in small numbers.

Artificial intelligence doesn’t have intuition and also can’t find relations between processes unless those are very obvious and have obvious pattern. Human brain is hard to simulate because we still don’t know everything about it and even if we would do, it would be to complex to make simulation of it.

source: Here

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