Programming Leftovers
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How to profile a FastAPI asynchronous request
I have been experimenting with FastAPI recently, a Python API framework self-describing as "high performance, easy to learn, fast to code, ready for production".
One of the features I wanted my project to have is to be fully asynchronous, from the app server to the SQL requests. As the API is mostly I/O bound, this would allow it to handle many concurrent requests with a single server process, instead of starting a thread per request, as one commony seen with Flask/Gunicorn.
However, this poses a challenge when it comes to profiling the code and interpreting the results.
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Plakar: vfs importer interface
I reworked the virtual filesystem layer in plakar, making it possible to write custom importers of data: use plakar to backup an s3 bucket, for instance.
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Qt Creator 11.0.1 released
We are happy to announce the release of Qt Creator 11.0.1!
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Changing default Host and Port in React.JS
React.js, a powerful JavaScript library developed by Facebook, is used to build user interfaces, especially for single-page applications. You’ll often use it to build complex UIs out of small, reusable pieces called “components”.
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MicroLisp: Lisp For Microcontrollers Now Has Lisp-Based ARM Assembler
In a way it feels somewhat silly to market a version of Lisp as targeting resource-constrained platforms, considering the systems it ran on back in the 1960s, but as time goes on, what would have given 1970s Big Iron a run for its money is now a sub-$5 microcontroller that you can run uLisp (MicroLisp) on. This particular project now even has an ARM assembler that is written in Lisp whose source code (GitHub) fits on a mere two A4-sized pages.
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Non-Capturing Groups in Python: An Unveiling of RegEx Secrets
Explaining the Regular Expressions (RegEx) Regular expressions, also known as regex or regexp, are a powerful tool used to manipulate and match strings of text in programming languages. Regex is a sequence of characters that define a search pattern.
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Lookahead in Python Regular Expressions: A Comprehensive Exploration
Regular expressions (regex) are powerful tools for text processing and pattern matching. In Python, regex is implemented through the re module, which provides a vast array of functions for matching and manipulating patterns in text data.
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The Art of Lookbehind in Python Regular Expressions
Regular expressions are one of the most important tools for data processing and text manipulation. They are used in various fields such as web development, data analysis, and natural language processing. One of the most powerful features of regular expressions is the ability to perform lookbehind assertions.
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Unlocking Python’s findall(): A Guide to Regular Expressions
Regular expressions are a powerful tool in programming that allow you to match patterns in strings. They are a language unto themselves with their own syntax and rules, but they can be incredibly useful when working with text data.
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Exploring fullmatch() in Python: A Deep Dive into Regular Expressions
Regular expressions are a powerful tool in programming that allow developers to search for and manipulate text with extreme precision. They are essentially a set of rules and patterns that programming languages can use to match and process text data.
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The Power of Python’s finditer(): A Comprehensive Guide
Python is one of the most popular programming languages in use today, and for good reason. It is versatile, relatively easy to learn, and has a vast ecosystem of libraries and tools.
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Mastering match() in Python: A Guide to RegEx Functions
Regular expressions (RegEx) are a sequence of characters that define a search pattern. It is widely used in computer programming to find and match patterns in text strings. Regular expressions have become an essential tool for developers, especially when working with large amounts of data and text parsing tasks.
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Unraveling Python’s search() Function: A Deep Dive into Regular Expressions
Introduction Regular expressions are an important part of programming, allowing developers to manipulate and analyze text data with ease.
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Reuse annotate automation
Someone pointed out that it would be great to automate the addition of the metadata inside the files based on the git repository.
So here is a small script that does exactly that. It goes over all your .cpp and .h file and will add the header based on the list of authors as well as the first commit on that particular file.
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How the rc shell handles whitespace in various contexts
I recently read Mark Jason Dominus's The shell and its crappy handling of whitespace, which is about the Bourne shell and its many issues with whitespace in various places. I'm a long time user of (a version of) Tom Duff's rc shell (here's why I switched), which was written for Research Unix Version 10 and then Plan 9 to (in part) fix various issues with the Bourne shell. You might ask if rc solves these whitespace issues; my answer is that it about half solves them, and the remaining half is a hard to deal with problem area (although the version of rc I use doesn't support some things that would help).