Tag: Terry Lambert

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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.

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.)

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

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

Malbolge 2022 2023

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

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

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

When you ask Siri a question, she gives you an answer. But have you ever stopped to wonder how she knows the answer? After all, she’s just a computer program, right? Well, actually, Siri is powered by artificial intelligence (AI) and Machine Learning (ML). This means that she constantly learning and getting better at understanding human speech. So when you ask her a question, she uses her ML algorithms to figure out what you’re saying and then provides you with an answer.

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

The Amazon Echo is a voice-activated speaker powered by Amazon’s AI assistant, Alexa. Echo uses far-field voice recognition to hear you from across the room, even while music is playing. Once it hears the wake word “Alexa,” it streams audio to the cloud, where the Alexa Voice Service turns the speech into text. Machine learning algorithms then analyze this text to try to understand what you want.

But what does this have to do with spying? Well, it turns out that ML can also be used to eavesdrop on people’s conversations. This is why many people are concerned about their privacy when using voice-activated assistants like Siri, Alexa, and Ok Google. However, there are a few things that you can do to protect your privacy. For example, you can disable voice recognition on your devices or only use them when you’re in a private location. You can also be careful about what information you share with voice-activated assistants. So while they may not be perfect, there are ways that you can minimize the risk of them spying on you.

Some applications which have background components, such as Facebook, do send ambient sounds to their data centers for processing. In so doing, they collect information on what you are talking about, and use it to target advertising.

Siri, Google, and Alexa only do this to decide whether or not you’ve invoked the activation trigger. For Apple hardware, recognition of “Siri, …” happens in hardware locally, without sending out data for recognition. The same for “Alexa, …” for Alexa hardware, and “Hey, Google, …” for Google hardware.

Things get more complicated for these three things, when they are installed cross-platform. So, for example, to make “Hey, Google, …” work on non-Google hardware, where it’s not possible to do the recognition locally, yes, it listens. But unlike Facebook, it’s not recording ambient to collect keywords.


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Practically, it’s my understanding that the tree major brands don’t, and it’s only things like Facebook which more or less “violate your trust like this. And other than Facebook, I’m uncertain whether or not any other App does this.

You’ll find that most of the terms and conditions you’ve agreed to on installation of a third party App, grant them pretty broad discretion.

Personally, I tend to not install Apps like that, and use the WebUI from the mobile device browser instead.

If you do that, instead of installing an App, you rob them of their power to eavesdrop effectively. Source: Terry Lambert

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

Conclusion:

Machine learning is a field of artificial intelligence (AI) concerned with the design and development of algorithms that learn from data. Machine learning algorithms have been used for a variety of tasks, including voice recognition, image classification, and spam detection. In recent years, there has been growing concern about the use of machine learning for surveillance and spying. However, it is important to note that machine learning is not necessarily synonymous with spying. Machine learning algorithms can be used for good or ill, depending on how they are designed and deployed. When it comes to voice-activated assistants such as Siri, Alexa, and OK Google, the primary concern is privacy. These assistants are constantly listening for their wake words, which means they may be recording private conversations without the user’s knowledge or consent. While it is possible that these recordings could be used for nefarious purposes, it is also important to remember that machine learning algorithms are not perfect. There is always the possibility that recordings could be misclassified or misinterpreted. As such, it is important to weigh the risks and benefits of using voice-activated assistants before making a decision about whether or not to use them.

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