I wrote a small e-book about terrible tips for C++ developers. Actually, it describes bad programming practices and explains why it's better to avoid them. However, every chapter of this mini-book starts with a terrible tip — just for fun.

By the way, these tips may seem artificial but believe me, they are based on the real experience. In other words, the described terrible tips occur in developers' lives — that's why it's worth discussing them. First of all, this book will be useful for junior developers. But more skilled C++ developers can also find interesting and useful tips.

Even though it's a mini-book, it clearly does not fit into the Habr format. Too many words. So, I decided to write here the review. Here is the link to find the full version of the mini-book: 60 terrible tips for a C++ developer.

If you still hesitate whether to read it or not, below you will find a list of terrible tips that will be discussed in the mini-book.

View the terrible tips:

Background

As a product manager with over a decade of experience, I'm always looking for new ways to enhance my skills and help other product managers advance in their careers. One area that many companies focus on during the job interview process is analytical and creative problem-solving. And as product managers, we encounter these types of challenges on a daily basis. It's important to stay sharp and continuously develop our problem-solving abilities. That's why I made it a habit to practice a logic puzzle every day. But I also wondered if there was a common approach to solving these puzzles. That's when I discovered TRIZ – the Theory of Inventive Problem Solving. In this article, I'll provide an overview of what TRIZ is and how it can be applied in product management. Then, I'll apply TRIZ principles to solve a series of logical puzzles, showcasing the power and effectiveness of this methodology. So whether you're an experienced product manager looking to enhance your skills or someone who enjoys a good brain teaser, read on to discover the power of TRIZ!

If you find TRIZ to be a useful tool for problem-solving and innovation, there are many resources available to help you delve deeper into the methodology. The TRIZ Journal, for example, offers enough information on TRIZ, including case studies, articles, and other resources. You can also find books and online courses that provide a more in-depth look at TRIZ and how it can be applied in different industries and contexts. So if you're interested in learning more, there are plenty of opportunities to expand your knowledge and apply TRIZ to your work.

Unlock the power of Transformer Neural Networks and learn how to build your own GPT-like model from scratch. In this in-depth guide, we will delve into the theory and provide a step-by-step code implementation to help you create your own miniGPT model. The final code is only 400 lines and works on both CPUs as well as on the GPUs. If you want to jump straight to the implementation here is the GitHub repo.

Transformers are revolutionizing the world of artificial intelligence. This simple, but very powerful neural network architecture, introduced in 2017, has quickly become the go-to choice for natural language processing, generative AI, and more. With the help of transformers, we've seen the creation of cutting-edge AI products like BERT, GPT-x, DALL-E, and AlphaFold, which are changing the way we interact with language and solve complex problems like protein folding. And the exciting possibilities don't stop there - transformers are also making waves in the field of computer vision with the advent of Vision Transformers.

Explaining through simple examples

For a long time, people have been thinking on how to create a computer that could think like a person. The advent of artificial neural networks is a significant step in this direction. Our brain consists of neurons that receive information from sensory organs and process it: we recognize people we know by their faces, and we feel hungry when we see delicious food. All of this is the result of brain neurons working and interacting with each other. This is also the principle that artificial neural networks are based on, simulating the processes occurring in the human brain.

What are neural networks

Artificial neural networks are a software code that imitates the work of a brain and is capable of self-learning. Like a biological network, an artificial network also consists of neurons, but they have a simpler structure.

If you connect neurons into a sufficiently large network with controlled interaction, they will be able to perform quite complex tasks. For example, determining what is shown in a picture, or independently creating a photorealistic image based on a text description.