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There are two articles on Russian, the author of which writes a virtual machine (interpreter) for executing a simple bytecode and then applies different optimizations to make this virtual machine faster. Besides that, there is a compiler of a simple C-like language into this bytecode. After reading this article and getting familiar with the compiler, I thought that it would be interesting to try writing a virtual machine for this language that would be able to apply JIT-compilation to this bytecode with the libjit library. This article describes the experience of doing that.

I found several articles online that describe the usage of this library, but those that I saw, describe the compilation of concrete programs with libjit, while I was interested in compiling arbitrary bytecode. For people interested in further reading, there is an official titorial, a series of articles and a series of comparisons (in Russian).

The implementation was done in C++ because we aren`t playing games here. All my code is in my repository. The "main" branch has just the interpreter of the PigletVM bytecode; "labels-with-fallbacks" has a partial JIT compilation implementation (that doesn`t support JUMP instructions), "full-jit" has fully working JIT-compilationl; "making-jit-code-faster" makes code generated by JIT work faster and "universal-base-vm*" branches merge the interpreter and JIT-compilation implementations, by implementing a base generalised executor, which can be used for different implementations of PigletVM (both the interpreter and libjit compilation)

Multidisciplinary project emerges when multiple teams with different expertise areas join to create a product. Despite the fact the product development is not something happining merely my a wish, product leads often perceive it as an easy walk. Usually this easy walk becomes a crash course. Let's uncover what leads to crash and what is necessary to succeed.

When using older version of Java with not updated truststore, you may face an error while connecting to web hosts, that Java is unable to find a valid certification path to the requested target.

This happens because JRE truststore is unaware of the new root certificate that is being used by Let's Encrypt nowadays. Below I tried to clarify detail behind this issue and how to solve it. I hope newcomers might find this material helpful.

ChatGPT was not used in writing this article. The animated image uses the webp file format instead of gif.

In this article, you will find a complete description of how to make a Wi-Fi internet radio receiver from a router that can play mp3 streams from internet radio stations. It is also possible to switch between two internet radio stations. We will use OpenWRT firmware installed on the router to create a Wi-Fi internet radio. It is possible to complete this project without using a soldering iron. All the components can be placed inside the router to create a finished device — a Wi-Fi internet radio.

• Any router with a USB port that can be installed with OpenWRT, a Linux-based operating system designed for embedded systems. You can check if your router is compatible with OpenWRT on this page: https://openwrt.org/toh/start.
• A USB sound card that costs $1-$2.
• Any passive (or active) speakers.
• Two patch cords — twisted pair crimped with 8P8C connectors (also known as RJ-45).
• A personal computer.
• Ethernet and Wi-Fi internet connection (optional).

This article describes the new Nginx Unit web server. In it you can learn more about the web server itself, its installation and configuration: how to use listeners, routing, how to install TLS certificates. The article will show how easy it is to work with it and that huge configs are slowly becoming a thing of the past.

In the rapidly evolving landscape of digital entertainment, the year 2023 has witnessed a profound transformation in Video On Demand (VOD) Over-The-Top (OTT) platforms. These platforms have redefined how audiences access and consume content, introducing cutting-edge technologies and innovative features that have revolutionized the entertainment industry.

Embracing Augmented Reality (AR) and Virtual Reality (VR)

In 2023, VOD OTT platforms have embraced AR and VR technologies to provide viewers with immersive and interactive experiences. Users can now step into virtual worlds, interact with characters, and actively participate in the content they are watching, transcending the boundaries of traditional passive viewing.

Imagine exploring an ancient civilization through the eyes of a protagonist, feeling the thrill of an adrenaline-pumping car chase, or even interacting with virtual avatars of celebrities and influencers. The integration of AR and VR into VOD OTT platforms has ushered in a new era of interactive and experiential entertainment.

Personalization: The Heart of the VOD Experience

Powered by advanced artificial intelligence (AI), VOD OTT platforms have taken personalization to new heights. By analyzing user behavior, preferences, and viewing habits, these platforms deliver tailor-made content recommendations, ensuring that viewers find exactly what they love without extensive searching.

This level of personalization ensures that users are presented with content that resonates with their tastes, creating a seamless and enjoyable content discovery experience. By removing the need for extensive searching, VOD OTT platforms have become the go-to destination for viewers seeking highly relevant and engaging content.

It may seem obvious but lambda has to have permission to s3/dynamodb

Create API Gateway REST API with Lambda as handler

don’t forget to enable CORS, attach custom domain

Let’s imagine you need access to the real-time data of some smart contracts on Ethereum (or Polygon, BSC, etc.) like Uniswap or even PEPE coin to analyze its data using the standard data scientist/analyst tools: Python, Pandas, Matplotlib, etc. In this tutorial, I’ll show you more sophisticated data access tools that are more like a surgical scalpel (The Graph subgraphs) than a well-known Swiss knife (RPC node access) or hammer (ready-to-use APIs). I hope my metaphors don’t scare you 😅.

Arbeiten im technischen Support brachte zusätzlich zu allen Aufgaben die Pflicht mit sich, die Kommunikationskanäle zu überwachen. Dies wurde über den Grafana-Dienst realisiert, der die erforderlichen Metriken aus Zabbix bezog. Da die Art der Arbeit jedoch bedeutete, dass man nicht immer an seinem Arbeitsplatz sitzt, kam mir die Idee, dies ein wenig zu automatisieren und Benachrichtigungen auf das Telefon oder zum Beispiel in einen Messenger zu erhalten, falls ein Kommunikationskanal ausfällt. Allerdings hatte ich keinen Zugriff auf das Zabbix-System und auch keinen erweiterten Zugriff auf Grafana.

gookit/goutil An extended library of Go's common functionality. Contains: number, string, slice/array, Map, struct, reflection, text, file, error, time and date, test, CLI, command run, system information, formatting, common information acquisition, etc.

It hasn't been that long since my last cheat sheet was published and this is the day for the new one. This time I got a task to find a solution for backing up Kafka to S3 bucket, following the terms:

I'm Alex Movergan, DevOps team lead at Altenar. I focus on automation in general and on improving troubleshooting skills within my team. In this article, I'll share a captivating tale that revolves around Kubernetes, validation webhooks, kubespray, and Calico.

Join me on this DevOps journey as we explore real-world scenarios unraveling the intricacies of troubleshooting in a Kubernetes environment.

This article aims at explaining the mathematical sense of the Principal Component Analysis (PCA) in practice.

In my previous article, I showed you how to deploy your project to Firebase and use it for free. Now, let’s explore additional benefits of Firebase. In the upcoming article, I will show you how to set up a custom domain name for your project and utilize a free SSL certificate from Firebase.

Brief problem formulation

The program accepts as input the absolute path to the image in the bmp extension and the path where you save the result of the work. Then, it rotates the image by 90 degrees counterclockwise. Afterwards, the program saves the new image.

The program is executed on C.

Explaining main algorithm.

For a while I’ve been thinking of writing a scientific article. I wanted it to have certain utility.

Morse code is binary: it takes only two values – either dot (short) or hyphen (long). I figured out that short (s) can stand for two-eye blinking whilst long (l) can indicate left-eye blinking. Another question emerged: how to understand when does one-symbol recording stop?

Empty space between two symbols can be presented by right-eye blinking – r. If I input singly symbol of short (dot) and long (hyphen), I will blink my right eye once to indicate the space between two symbols.

To separate independent words, one has to blink her right eye twice and get rr.

Hence, I have collected an ordered set of symbols – r, l, s, - that can be converted into a full-fledged text. Once I accomplish the transformation, I get an answer.

Task: To provide automation for transfer of large number of files.

Source - computer with autotest codebase.

Receiver - gateway for industrial data processing.

Test receiver - second PC with installed vsftpd service.

The most important stage in the data science process is feature engineering, which entails turning raw data into useful features that might enhance the performance of machine learning models. It calls for creativity, data-driven thinking, and domain expertise. Data scientists can improve the prediction capability of their models and find hidden patterns in the data by choosing, combining, and inventing relevant features. Handling missing data, scaling features, encoding categorical variables, constructing interaction terms, and other procedures are examples of feature engineering techniques. The best practises involve investigating the data, testing and improving features iteratively, and applying domain knowledge to draw out important information. The accuracy and effectiveness of machine learning models are significantly influenced by effective feature engineering.

How do people usually write a server if they don't really care about performance? A program starts, then starts accepting incoming connections from clients and starts a new thread for each client, which is engaged in servicing this client. If you use framework, like Spring or Flask or Poco there, then it does something like this inside itself - the only difference is the threads can be reused, that is, taken from a certain pool. It's all quite convenient, but not too effective (and Spring is bad). Most likely, your threads serving clients do not live long and most of the time they are waiting either to receive data from the client or to send it to the client - that is, they are waiting for some system calls to return. Creating an OS thread is quite an expensive operation, as is context switching between OS threads. If you want to be able to serve a lot of customers efficiently, you need to come up with something else. For example, callbacks, but they are pretty inconvenient (though there are different opinions on this).

Another option is to use non-blocking I/O in combination with some kind of implementation of user-space threads (fibers). In this article I will show you how to write all this with your own hands.

As a product owner, it is common to face the question of whether to proceed with option A or option B. Or, which version of the screen should be implemented to achieve better results? Making such decisions can be challenging, especially when you are under tight deadlines with limited resources. Furthermore, such decisions are made based on personal judgment or copying the approach of a competitor, which can lead to suboptimal results.

The good news is that one can avoid such pitfalls by setting up a simple experiment environment that requires relatively low effort. In this article, we will describe how you can achieve this.