Programming Leftovers
Shell/Bash/AWK
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How to Use $IFS in Bash?
The IFS is an abbreviation for Internal Field Separator which is an environment variable in the shell to determine the separator. The $IFS specifies how the words on the command prompt are delimited. By default, field separators (IFS) are the space, tab, and newline but we can alter this in our script to match the requirements. It is mostly used for loops to manipulate the elements in the specified list. We use the $IFS variable in our Bash script to split the string because Bash does not include a built-in mechanism for string splitting.
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Delete a Folder in Bash
There are two alternative ways to delete a directory, file, or folder in Bash. We can delete the folders or directories using two different commands. The “rmdir” command is used to delete the empty folders or directories. The “rm” command is used to delete the folders or directories that are not empty. However, we will use the “rm” command to delete the folders in this article. Additionally, we delete some files from the Linux desktop.
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What is AWK NF?
The “AWK” function is a Linux utility that is used for text analysis that offers strong data control. To use the Linux computer program and “AWK” function, users can evaluate, modify, and generate the prepared results. The “AWK NF” variable is used to show how many files, elements, or other items are contained in a line of any file.
Python
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The Python on Hardware Newsletter: Happy New Year, please subscribe now
The Python on Microcontrollers newsletter is the place for the latest news involving Python on hardware (microcontrollers AND single board computers like Raspberry Pi).
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John Park’s CircuitPython Parsec: Using Super Nintendo Controllers
How to use Super Nintendo (SNES) controllers in CircuitPython with the keypad library.
Perl / Raku
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Keeping Your Valuables Under Lock and Key
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2023.01 Humming Away - Rakudo Weekly News
Rawley Fowler has created a simple web-framework called Humming-Bird, inspired mainly by Opium, Sinatra and Express. And introduced it in a post on /r/rakulang, with quite a few reactions.
[...]
Anton Antonov released a new module called DSL::Bulgarian allowing one to specify computational workflows using natural language commands in Bulgarian, and introduced it in a dedicated blog post.
Misc.
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Niepce December 2022 updates - Hubert Figuière
Here is some udpdate on Niepce work done in December 2022. Mostly changes under the hood, but important ones to move forward with improving the features. The short version: it feels great to remove C++ code.
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Commercial LTS Qt 6.2.7 Released [Ed: conflating non-free/proprietary with "commercial"]
We have released Qt 6.2.7 LTS for commercial license holders today. As a patch release, Qt 6.2.7 does not add any new functionality but provides bug fixes and other improvements.
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Std List C++
In C++, std::list is like a container that stores the elements on the non-contagious memory location. The “std::list” is implemented as a doubly linked list. We can insert or remove any element of the list that is stored in any location, which makes the lists bidirectional and sequential. Bidirectional because we can access the list elements from the front and back of the list (from any location). Sequential because we can access the elements anywhere in the sequence. Lists behave as doubly linked lists. Doubly linked lists can store their elements anywhere in the memory and they can be accessed from any location on the runtime. The list belongs to a standard template library like arrays and vectors but lists do not allow a fast access comparable to other data structures. Let’s discuss “std::list” in this article with the practical implementation of lists with different operations.
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2023 Predictions
The loss function in optimization is a function that serves as a proxy for the underlying performance measurement. In many cases, it's one of the most important components of any form of machine learning.
It's also sometimes referred to as the cost function, objective function, error function, or reward function, depending on what you're doing. Those terms capture the essence of what I'd like to get out of my 2023 predictions – a measured error and a way to derive a path to improvement (i.e., a metaphorical gradient).