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There is an area of Python that many developers have problems with. This is an area that has seen many different solutions pop up over the years, with many different opinions, wars, and attempts to solve it. Many have complained about the packaging ecosystem and tools making their lives harder. Many beginners are confused about virtual environments. But does it have to be this way? Are the current solutions to packaging problems any good? And is the organization behind most of the packaging tools and standards part of the problem itself?

Join me on a journey through packaging in Python and elsewhere. We’ll start by describing the classic packaging stack (involving setuptools and friends), the scientific stack (with conda), and some of the modern/alternate tools, such as Pipenv, Poetry, Hatch, or PDM. We’ll also look at some examples of packaging and dependency-related workflows seen elsewhere (Node.js and .NET). We’ll also take a glimpse at a possible future (with a venv-less workflow with PDM), and see if the PyPA agrees with the vision and insights of eight thousand users.

“What happens when you open a web browser and enter google.com?” Many of us recall being asked this question before. I think it leaves an impression because navigating web pages is this magical process that we take for granted. We do it hundreds, if not thousands of times per day without knowing how it works. Most developers and engineers can explain parts of it, but the depth at which you can discuss this question is infinite.

Today, we’ll discuss the details of something else we take for granted: the terminal. What happens when you open a terminal emulator and enter “ls”? Like with browsers, there is too much content to fit into one blog post. We’ll give you what we think are the interesting details.

As I type these words, a mass exodus is underway from Twitter and Facebook. After decades of eye-popping growth, these social media sites are contracting at an alarming rate.

In some ways, this shouldn’t surprise us. All the social networks that preceded the current generation experienced this pattern: SixDegrees, Friendster, MySpace, and Bebo all exploded onto the scene. One day, they were sparsely populated fringe services, the next day, every­one you knew was using them and you had to sign up to stay in touch. Then, just as quickly, they imploded, turning into ghost towns, then punchlines, then forgotten ruins.

Artificial intelligence has made tremendous advances since its inception about seventy years ago. Self-driving cars, programs beating experts at complex games, and smart robots capable of assisting people that need care are just some among the successful examples of machine intelligence. This kind of progress might entice us to envision a society populated by autonomous robots capable of performing the same tasks humans do in the near future. This prospect seems limited only by the power and complexity of current computational devices, which is improving fast. However, there are several significant obstacles on this path. General intelligence involves situational reasoning, taking perspectives, choosing goals, and an ability to deal with ambiguous information. We observe that all of these characteristics are connected to the ability of identifying and exploiting new affordances—opportunities (or impediments) on the path of an agent to achieve its goals. A general example of an affordance is the use of an object in the hands of an agent. We show that it is impossible to predefine a list of such uses. Therefore, they cannot be treated algorithmically. This means that “AI agents” and organisms differ in their ability to leverage new affordances. Only organisms can do this. This implies that true AGI is not achievable in the current algorithmic frame of AI research. It also has important consequences for the theory of evolution. We argue that organismic agency is strictly required for truly open-ended evolution through radical emergence. We discuss the diverse ramifications of this argument, not only in AI research and evolution, but also for the philosophy of science.

The art of good programming depends upon the discipline of the programmer, no matter what language is being used. The purpose of object oriented programming (OOP) is to produce well designed reusable code. In principle OOP can be done in any language, even assembly. This is because all OO language compilers/assemblers (e.g. C++) ultimately translate the high level constructs of the language into machine language. Thus there is a mapping from an object oriented semantics onto the instruction and data arrays that are executable images.

Here we will present a design and implementation method for producing OO code in the C language. It turns out that using this methodology will strongly improve your overall program design and implementation just as you expect when programming in a native OO language like Java or C++. When working in C, however, the discipline applied to producing good designs comes from the programmer and not from the language itself.

A curated list of awesome Open-Source Intelligence (OSINT) Resources - GitHub - ARPSyndicate/awesome-intelligence: A curated list of awesome Open-Source Intelligence (OSINT) Resources

1. I teach and preach the system based on “no,” which in a negotiation simply means maintaining the status quo.

2. My “No” system is a set of clear principles and practices that you follow step by...