It doesn’t have to be that way. In fact, sobriety has given me the clarity to start confronting these issues in a way I
Julia allows you to avoid some of these problems. Despite being dynamically typed, it has a just-in-time compiler. The JIT compiler either generates the machine code right before it’s executed or uses previously stored, cached compilations, which makes it as performant as statically typed languages. More importantly, it has a key feature known as multiple dispatch that is like function overloading of OOPs, albeit at runtime. The power of multiple dispatch lies in its ability to handle different argument types without the need to create separate function names or nested if statements. This helps in writing compact code, which is a big win in numeric computations since unlike Python, you can easily scale solutions to deal with all types of arguments.
A huge part of my recovery has been learning to be completely honest, even if people will see that I’m not following societal rules and norms. Letting go and not caring when people judge me for it is a key part of building confidence and comfort in sobriety.
Those of us who have a deeply-ingrained desire to be seen as a good kid develop long-term issues. We end up doing the rulebreaking in secret. We establish harmful patterns early on in our relationship to alcohol that perpetuates a cycle of addiction.
But we can learn to break free from this in many ways. Take baby steps in breaking small, inconsequential rules and see that nothing bad happens. Be honest about it with someone, and see that they don’t judge you for it.
That’s how ridiculous this secrecy can be about even the smallest things. The rule that a weekday is a workday is completely made up. It’s not even true. Yet I still have a compulsion to be seen as following it.
Worse, unlike compiled code, Python’s interpreter analyzes every line of code at execution time. This leads to an overhead that causes a significantly slower performance when compared to other languages.
Life isn’t a set a rules, and so of course we turn to substances and methods of escape when we can’t seem to live up to those rules. Of course, we become secretive about it if we think we’ll get in trouble or be judged.
All good things come at a cost, and Python’s dynamically typed nature is no exception. It hampers developers, especially when running the code in production. Dynamic typing that makes it easy to write code quickly without defining types increases the risk of running into runtime issues, especially when the codebase size increases. Bugs that a compiler would easily figure out could go unidentified in Python, causing hindrances in production and ultimately slowing the development process in large-scale applications.
Why would someone admit to that? In my head, weekdays are workdays. You work on workdays. Even if you take the day off, you don’t admit to this. That would be admitting to breaking the rules.
One day, I had a six-mile run planned and on the schedule. I wasn’t feeling it and only went five. When I got back, my husband asked how far I went. He didn’t care. He didn’t even know what the plan was.
Rust is still trying to catch up with the machine learning community, and so I believe Swift and Julia are the languages that will dethrone Python and eventually rule data science. Let’s see why odds are against Python.
Even better, Swift is a statically typed language and is highly optimized due to its LLVM (Low-Level Virtual Machine) compiler. The LLVM makes it possible to quickly compile into assembly code, making Swift super-efficient and almost as fast as C. Also, Swift boasts better memory safety and management tools known as Automatic Reference Counting. Unlike garbage collectors, ARC is a lot more deterministic as it reclaims memory whenever the reference count hits zero.
Although the emphasis has been on addiction, this article is more about exploring a personality trait established very early on that increases the likelihood of addiction.
As compiled languages that offer type annotations, Swift and Julia are a lot faster and robust for development than Python. That alone might be enough to recommend them over the older language, but there are other factors to consider as well.
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launching this week for $729 and up, is Apple’s smallest phone in years. The company hasn’t released a phone as small as the 5.18-inch iPhone Mini since 2013, when it debuted the iPhone 5 line, which ranged in size from 4.87 to 4.9 inches.