Development

This world needs a new theory — a theory that could describe all the theories on the planet. A theory that could easily describe philosophy, mathematics, physics, and psychology. The one that makes all kinds of sciences computable.

This is exactly what we are working on. If we succeed, this theory will become the unified meta-theory of everything.

A year has passed since our last publication, and our task is to share the progress with our English-speaking audience. This is still not a stable version; it’s a draft. Therefore, we welcome any feedback, as well as your participation in the development of the links theory.

As with everything we have done before, the links theory is published and released into the public domain — it belongs to humanity, that means, it is yours. This work has many authors, but the work itself is far more important than any specific authorship. We hope that today it can become useful to more people.

We invite you to become a part of this exciting adventure.

How did engineers in the past manage to measure electrical power without modern microchips and DSPs? This article explores the Energomera CE6806P, a device created in 2006 for verifying electricity meters, yet built using 1980s-era technology.

We’ll take a closer look at its design, principles of operation, and how discrete-analog solutions were used to achieve high accuracy. The Energomera is a fascinating example of engineering and ingenuity, giving us a unique perspective on the evolution of electrical measurement devices.

While pg_probackup 3 is still in the works and not yet available to the public, let’s dive into what’s new under the hood. There’s a lot to unpack — from a completely reimagined application architecture to long-awaited features and seamless integration with other tools. 

Statistically, September CommitFests feature the fewest commits. Apparently, the version 18 CommitFest is an outlier. There are many accepted patches and many interesting new features to talk about.

If you missed the July CommitFest, get up to speed here: 2024-07.

Since the PostgreSQL 17 RC1 came out, we are on a home run towards the official PostgreSQL release, scheduled for September 26, 2024.

Let's take a look at the patches that came in during the March CommitFest.

Machine Learning (ML) technologies are becoming increasingly popular and have various applications, ranging from smartphones and computers to large-scale enterprise infrastructure that serves billions of requests per day. Building ML tools, however, remains difficult today because there are no industry-wide standardised approaches to development. Many engineering students studying ML and Data Science must re-learn once they begin their careers. In this article, I've compiled a list of the top five problems that every ML specialist faces only on the job, highlighting the gap between university curriculum and real-world practice. 

За последнее время я вижу всё больше споров на тему двух популярных GitOps инструментов: Argo CD и Flux CD.

На самом деле я считаю такие споры необоснованными, потому что глубоко убеждён что внимания заслуживают оба инструмента и каждый из них хорош для решения своего круга задач.

В своей профессиональной деятельности я активно использую и тот и другой. Я хочу поделиться с вами своим мнением и кейсами использования. Надеюсь эта статья поможет вам выбрать наиболее подходящий инструмент под ваши нужды.

Picture this: A thriving e-commerce platform faces a constant battle against fake reviews that skew product ratings and mislead customers. In response, the company employs cutting-edge algorithms to detect and prevent fraudulent activities. Solutions like these are crucial in the modern digital landscape, safeguarding businesses from financial losses and ensuring a seamless consumer experience.

The industry has relied on rules-based systems to detect fraud for decades. They remain a vital tool in scenarios where continuous collecting of a training sample is challenging, as retraining methods and metrics can be difficult. However, machine learning outperforms rules-based systems in detecting and identifying attacks when an ongoing training sample is available.

With advancements in machine learning, fraud detection systems have become more efficient, accurate, and adaptable. In this article, I will review several ML methods for preventing fraudulent activities and discuss their weaknesses and advantages.

The end of the March Commitfest concludes the acceptance of patches for PostgreSQL 16. Let’s take a look at some exciting new updates it introduced.

I hope that this review together with the previous articles in the series (2022-07, 2022-09, 2022-11, 2023-01) will give you a coherent idea of the new features of PostgreSQL 16.

Programming textbooks usually do not indulge us with variety of examples. In most manuals, exercises are similar to each other and not particularly interesting: create another address book, draw a circle using turtle, develop a website for a store selling some kind of "necessary" advertising nonsense. Too far from the authentic imitation of "The Matrix". Although…

How about taking over the control and starting to invent exercises yourself?

Would you like to write your own personal little "Matrix"? Of course, not the one with skyscrapers, stylish phones of the time, and the ubiquitous invincible Agent Smiths. We will need a couple of more months of learning for that. But any beginner programmer can write a model of the cult splash screensaver with the green streams of digits flowing down the screen. Let's try to creat it in the "great and mighty" Python.

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.

Hi everyone, a few months ago I got a Brother AX-25, and since then, I've been working on turning it into a computer. It uses an Arduino to scan the custom mechanical keyboard and control the typewriter, and a Raspberry Pi is connected to the Arduino over serial so I can log into it in headless mode.

In our last article we made a bunch of promises about our speech synthesis.

After a lot of hard work we finally have delivered upon these promises:

• Model size reduced 2x;
• New models are 10x faster;
• We added flags to control stress;
• Now the models can make proper pauses;
• High quality voice added (and unlimited "random" voices);
• All speakers squeezed into the same model;
• Input length limitations lifted, now models can work with paragraphs of text;
• Pauses, speed and pitch can be controlled via SSML;
• Sampling rates of 8, 24 or 48 kHz are supported;
• Models are much more stable — they do not omit words anymore;

This is a truly break-through achievement for us and we are not planning to stop anytime soon. We will be adding as many languages as possible shortly (the CIS languages, English, European languages, Hindic languages). Also we are still planning to make our models additional 2-5x faster.

We are also planning to add phonemes and a new model for stress, as well as to reduce the minimum amount of audio required to train a high-quality voice to 5 — 15 minutes.

As usual you can try our model in our repo or in colab.

2021 was an action-packed year for Qrator Labs.

It started with the official celebration of our tenth year anniversary, continued with massive routing incidents, and ended with the infamous Meris botnet we reported back in September.

Now it is time to look at the events of the last quarter of 2021. There are interesting details in the BGP section, like the new records in route leaks and hijacking ASes, but first things first, as we start with the DDoS attacks statistics.

Comparing PHP-FPM, NGINX Unit, and Laravel Octane - what to choose for developing microservices.

DDoS attacks send ripples on the ocean of the Internet, produced by creations of various sizes - botnets. Some of them feed at the top of the ocean, but there also exists a category of huge, deep water monstrosities that are rare and dangerous enough they could be seen only once in a very long time.

November 2021 we encountered, and mitigated, several attacks from a botnet, that seems to be unrelated to one described and/or well-known, like variants of Mirai, Bashlite, Hajime or Brickerbot.

Although our findings are reminiscent of Mirai, we suppose this botnet is not based purely on propagating Linux malware, but a combination of brute forcing and exploiting already patched CVEs in unpatched devices to grow the size of it. Either way, to confirm how exactly this botnet operates, we need to have a sample device to analyze, which isn’t our area of expertise.

This time, we won’t give it a name. It is not 100% clear what we are looking at, what are the exact characteristics of it, and how big this thing actually is. But there are some numbers, and where possible, we have made additional reconnaissance in order to better understand what we’re dealing with.

But let us first show you the data we’ve gathered, and leave conclusions closer to the end of this post.

Hello, everybody!

My name is Alexander Zubkov and today I’d like to talk about routing loops.

The third quarter of 2021 brought a massive upheaval in the scale and intensity of DDoS attacks worldwide.

It all led to September when together with Yandex, we uncovered one of the most devastating botnets since the Mirai and named it Meris, as it was held accountable for a series of attacks with a very high RPS rate. And as those attacks were aimed all over the world, our quarterly statistics also changed.

This quarter, we've also prepared for your consideration a slice of statistics on the application layer (L7) DDoS attacks. Without further ado, let us elaborate on the details of DDoS attacks statistics and BGP incidents for Q3, 2021.

Introduction

For the last five years, there have virtually been almost no global-scale application-layer attacks.

During this period, the industry has learned how to cope with the high bandwidth network layer attacks, including amplification-based ones. It does not mean that botnets are now harmless.

End of June 2021, Qrator Labs started to see signs of a new assaulting force on the Internet – a botnet of a new kind. That is a joint research we conducted together with Yandex to elaborate on the specifics of the DDoS attacks enabler emerging in almost real-time.

• We’ll find out what is an in-memory database, or IMDB. When and how are they better than disk solutions?
• Then, we’ll consider their architecture. What about their efficiency, reliability, and scaling?
• Then, we’ll delve into technical details. Data types, iterators, indexes, transactions, programming languages, replication, and connectors.