Everyone knows the vector container of C++’s Standard Template Library (STL). It is useful, versatile, and store the data of all elements in a contiguous memory location. There is another container named std::valarray for array data that is not widely known. It is part of the STL for a long time (i.e. way before C++11). The use case is to perform operations on all array elements in parallel. You can even multiply two valarray containers element by element without using loops or other special code. While it has no iterators, you can easily create a valarray container from a vector, perform calculations in parallel, and push the results into a vector again. The C++ reference has example code to show how to do this. Creation from a vector requires access to the memory location of the vector’s data.
std::vector<double> values;
// Put some values into the vector here …
// Convert vector to valarray
std::valarray<double> val_values( values.data(), values.size() );
Now you can perform operations on all elements at once. Calculating cos() of all elements simply looks like this:
auto val_result = cos(val_values);
If you take the time and compare it to a loop through a vector where the function is called for every element, then you notice valarray is much faster. It depends on your compiler. GCC and Clang are quite fast. The apply() member function allows you to run arbitrary functions on every element. If you only need a subset of the elements, then you can create slices with the required values.
Testing software and measuring the code coverage is a critical ritual for most software development teams. The more code lines you cover, the better the results. Right? Well, yes, and no. Testing is fine, but you should not get excited about maximising the code coverage. Measuring code coverage can turn into a game and a quest for the highest score. Applying statistics to computer science can show you how many code paths your tests need to cover. Imagine that you have a piece of code containing 32 if()/else() statements. Testing all branches means you will have to run through 4,294,967,296 different combinations. Now add some loops, function calls, and additional if() statements (because 32 comparisons are quite low for a sufficiently big code base). This will increase the paths considerably. Multiply the number by the time needed to complete a test run. This shows that tests are limited by physics and mathematics.
Learning about programming first and then learning secure coding afterwards is a mistake. Even if you are new to a programming language or its concepts, you need to know what can go wrong. You need to know how to handle errors. You need to do some basic checks of data received, no matter what your toolchain looks like. This is part of the learning process. So instead of learning how to use code constructs or language features twice, take the shortcut and address security and understanding of the concepts at once. An example method of classes and their behaviour. If you think in instances, then you will have to deal with the occasional exception. No one would learn the methods first, ignore all error conditions, and then get back to learn about errors.
The words legacy and old carry a negative meaning when used with code or software development. Marketing has ingrained in us the belief that everything new is good and everything old should be replaced to ensure people spend money and time. Let me tell you that this is not the case, and that age is not always a suitable metric. Would you rather have your brain surgery from a surgeon with 20+ years of experience or a freshly graduated surgeon on his or her first day at the hospital?
Yesterday the CrowdStrike update disable thousands of servers and clients all across the world. The affected systems crashed when booting. A 
If all you have is a Large Language Model (LLM), then you will apply it to all of your problems. People are now trying to find 0-days with the might of LLMs. While there is no surprise that this works, there is a better way of pushing your code to the limit. Just use random data! Someone coined the term fuzzing in 1988. People have been using defective punch cards as input for a while longer. With input filtering of data, you want to eliminate as much bias as possible. This is exactly why people create the input data using random data. Human testers think too much, too less, or are too constrained. (Pseudo-)Random number generators rarely have a bias. LLMs do. This means that the publication about finding 0-days by using LLMs should not be good news. Just like human Markov chains, LLMs only „look“ in a specific direction when creating input data. The model is the slave of vectors and the training data. The process might use the source code as an „inspiration“, but so does a compiler with a fuzzing engine. Understanding that LLMs do not possess any cognitive capabilities is the key point here. You cannot ask an LLM what it thinks of the code in combination with certain input data. You are basically using a fancy data generator that uses more energy and is too complex for the task at hand.
Floating point data types are available in most programming languages. C++ knows about float, double, and long double data types. Other programming languages feature longer (256 bit) and shorter (16 bit and lower) representations. All data types are specified in the