The No Title^® Tech Blog

This is a pragmatic book that presents some of the most important topics about the Python programming language in a concise form, designed to make it easier to find out the most relevant information bits in a context where resources abound and frequently are just too overwhelming.

This time, we are releasing both Optimize Images and Optimize Images X at the same time. The original CLI version now uses temporary files with in-memory buffers, which prevents unnecessary I/O, and also displays more detailed and more useful version information. Optimize Images X, the GUI version, is now compatible with Python3.7+ and has the ability to open or preview a selected image.

So, just 3 days after the first public beta, here we got the second beta for Optimize Images X. It includes an important hot fix for Windows and Linux, and two new usability features.

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…