The No Title^® Tech Blog

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.

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.

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.