The No Title^® Tech Blog

Just for fun, this month I set myself to start a new Python project. It’s the same as Optimize Images, but with a big X and a nice graphical user interface. It wants to help you reduce the file size of images, and hopefully make your websites load faster.

You may wonder how I came up with this topic. I usually code in a graphical IDE like PyCharm or Visual Studio, or even a text editor like VS Code or BBEdit. But sometimes you need some lightweight application that is capable of opening and efficiently edit a file. I often return to VIM for quick edits in small files. Sometimes, just for the convenience of not leaving the shell. Other times, because it’s a powerful editor that runs perfectly under a SSH connection. And a few days ago, it was the only application I had installed on Windows which would let me open, navigate and read a several GB heavy text file without complaining.

Deep Reinforcement Learning is a somewhat new field within Machine Learning or Artificial Intelligence (you may pick your favorite term between these two, even if they’re not strictly the same), which combines Deep Learning and Reinforcement Learning and is based on the general idea that an agent can learn by observing its actions and their consequences. No, it is not a return to John B. Watson and B. F. Skinner’s behavioral psychology. We are talking, instead, about a set of pretty advanced machine learning algorithms that, when properly mastered, allow computers to achieve remarkable results in some complex tasks. That’s what this book is about, so let’s dive in…

Optimize Images has a new version just released on PyPI! Besides the usual clean and polish, this release includes two new features that may be of interest for you, especially if you are using this utility on servers.

If you’re a developer or a systems administrator, sooner or later you will meet that HUUUGE, GIANT, ENORMOUS file. A file that weights several gigabytes of more or less cryptic system or application logging messages, with no rotation scheme in place. Since log files are usually built by appending the most recent information to the end, probably you just need to check out the last 100 or 1000 lines, in order to find out why an application has crashed or failed to operate properly some seconds or a minute ago. Turns out that reading such huge files may not be as simple as editing them in Notepad. Probably, an application like that won’t even be able to the file. So, what else can you do?

Those among you who have already learned some Python or may even have used it in some projects will certainly have heard the expression “Pythonic Code”, which conveys a general and somewhat wide meaning of “clean code and good software development practices in the context of Python”. With Effective Python, the author presents you with nothing less than 90 practical examples on how to adopt a pythonic developer mindset and how to write better Python code.