Programming *

In this tutorial, I want to talk about the practical experience of native compilation of a production application written in Kotlin with Spring Boot and Gradle using GraalVM. I’ll start right away with the pros and cons of the native compilation feature itself and where it can be useful, and then I’ll move directly to the build process for MacOS and Windows.

At the end of the article, in the afterword block, I will talk in more detail about the project and why such a need arose, given quite a few limitations and pitfalls of supporting native compilation both from Spring Boot and from GraalVM.

Referential transparency, a key concept in functional programming, is often associated with more reliable, easier to test, and safer software. This term refers to a principle in which a function, given the same input, will always produce the same output without producing any side effects.

In the real world of software development, side effects are inevitable. Programs are rarely useful unless they interact with the outside world. This interaction could be reading from or writing to the console, making network requests, querying a database, or modifying a variable.

Yet, despite the necessity of side effects, they introduce risks and complexities. Programs with side effects are harder to test, harder to reason about, and more prone to bugs. They can also make the system as a whole more difficult to understand and maintain, due to hidden dependencies between components.

Enter referential transparency - a concept that means a function, given the same input, will always provide the same output, without creating any side effects. A function that adheres to this principle doesn't read any global state or change any state outside of its scope. The result is code that is more predictable and easier to reason about.

In terms of software safety and reliability, the absence of side effects is not enough. Programs should also be free from external influence - their results should only depend on their arguments. That is, programs should not read data from the console, a file, network, database, or even system variables.

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:

Faced with situation when had to map STRING VALUE coming from database TO ENUM. The problem happens when value stored in database differs from enum name. And here we need a workaround to make a mapping. I will describe main points on how I was able to realise it.

I believe that every programmer has at least once heard about ChatGPT and its marvelous abilities to process, calculate and create huge amounts of data; if not, go check out this Wikipedia article - https://en.wikipedia.org/wiki/ChatGPT.

Can you imagine that some 50 years ago people could not even believe that there may be something artificial surpassing humans in so many areas? Nowadays, we have this marvel at the distance of a few tabs on a phone screen or a keyboard; however, there is still a sadly large number of people who do not fully—if at all— utilize all the perks of ChatGPT in their lines of work. This is mostly related either to people's reluctance to learn new technologies or the fear of losing coding skills they have previously gained—which is not the case with using ChatGPT properly.

In this article I want to give you some of the most useful uses of ChatGPT for your coding work. Remember, there is nothing shameful in using the AI, since this the development and further implementation of it in our day-to-day life is inevitable, so we should start adapting to it as early as we can to take the full advantage of this "magical" technology. Let's get started.

Hi, my name is Mikhail Emelyanov, I’m a Python programmer and I would like to show you my pet project — Flywheel, a micro-platform for learning foreign languages, a mixture of Duolingo and Anki, an application that can teach you to properly write in Spanish (or any other language you’re studying). Flywheel’s source code is available on GitHub.

As you may know, generalized knowledge of a foreign language can be broken down into four relatively independent components: reading, writing, listening, and speaking. Unfortunately, training one of these abilities has no direct effect on the other components, so, for example, by developing our reading skills, the effect on our writing skills is quite indirect. Flywheel is a ‘sharpener’ specifically for written Spanish.

If you’ve ever used Duolingo, you should have some idea of the format in which you’ll be studying. The formula is simple: here’s a phrase, translate it into the other language; the app will remember the last time you translated a phrase and how successful you were at it; and depending on the accuracy of your answer, it will determine when you should do the same phrase again. In my opinion, Duolingo and its approach are brilliant. However… There are certain aspects that somewhat spoil the learning experience, and Flywheel was specifically designed to address them.

I have been working in Agile since 2017 in several projects.
And I would like to note here a couple of moments from real experience through the eyes of developer role in project.
Hope it will be helpful for you!

lsFusion platform is designed for rapid development of business applications. It is distributed under the terms of a Lesser General Public License (LGPLv3). The source code of the platform is available on Github.

lsFusion is best suited for creating complex systems with large numbers of entities and forms, where users need to input and process large amounts of data. However, the platform can also be used to quickly create simple applications instead of spreadsheets when Excel’s functionality is not enough.

At the same time the use of the platform will not give a great advantage when developing applications aimed at interaction with a large number of “external” users or without the need for any complex calculations. You should also take into account that the web interface is a single page application using JavaScript. Therefore, the lsFusion platform is not well-suited for creating websites, for example.

Faced with the situation when calling Mono::block leads to deadlock - in debug I can just see how it stands on Unsafe::park and no more movement at all.

There are such types in Java - called "Generics".
And the point here is that there is a convention for naming generic type parameters WITH ONE CAPITAL LETTER.

But we have found much more readable approach!

The problem

Unfortunately, when fulfilling their planned business goals, the departments of the organisation rarely take into account such a metric as solution code quality. And usually developers has no time for normal code review process.

In one of the tasks on the project, I had to wrap the ForkJoinTask collection in CompletableFuture for asynchronous execution and building data processing pipeline chains.

The life of Langton's Ant seems sad and lonely, but, as we'll soon discover, he is not ready to put up with such an outrageous situation and is trying his best to escape. American scientist Christopher Langton invented his ant back in 1986. Since then, no one has been able to explain the strange behavior of this mysterious model...

In this tutorial we'll learn how to write cross-platform code in C using the system functions on popular OS (Windows, Linux/Android, macOS & FreeBSD): file management functions and file I/O, console I/O, pipes (unnamed), execution of the new processes. We're going to write our own small helper functions on top of low-level userspace system API and use them so that our main code can run on any OS without modifications. This tutorial is Level 1, the easiest. I split the difficult stuff into parts so the code samples here shouldn't be overwhelming for those who has just started to program in C. We'll discuss the differences between the system API and how to create a cross-platform programming interface that hides all those differences from the user.

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.

This is a translation of my own article

The release of NewLang language with a brand new "feature" is coming, a remodeled version of the preprocessor that allows you to extend the language syntax to create different DSL dialects using macros.

What is it about?

DSL (Subject Oriented Language) is a programming language specialized for a specific application area. It is believed that the use of DSL significantly increases the level of abstractness of the code, and this allows to develop more quickly and efficiently and greatly simplifies the solution of many problems.

Conditionally, we can distinguish two approaches to DSL implementation:

• Development of independent syntax translators using lexer and parser generators to define the grammar of the target language through BNF (Backus–Naur form) and regular expressions (Lex, Yacc, ANTLR, etc.) and then compiling the resulting grammar into machine code.
• Development or integration of the DSL dialect into a general-purpose language (metalanguage), including the use of various libraries or special parsers / preprocessors.

We will talk about the second option, namely the implementation of DSL on the basis of general-purpose languages (metalanguages) and the new implementation of macros in NewLang as the basis for DSL development.

As a Product Manager who has worked on the development of delivery route optimisation software for 10+ years, I see that modern technologies can significantly improve the optimisation process and deliver better solutions. AI, machine learning, and other modern technologies have the potential to revolutionise the way delivery routes are optimised in the future.

With the increasing availability of data and the advancement of AI and machine learning algorithms, it is becoming possible to develop more sophisticated prediction models that can be integrated into optimisation algorithms to make more accurate and informed decisions about route planning and scheduling. Machine learning algorithms can be trained to predict customer demand based on historical sales data and other market trends, allowing businesses to optimise their delivery schedules and routes accordingly. AI can also be used to optimise delivery schedules based on customer preferences and other relevant factors.

Blockchain technology could be used to create a secure, decentralised database of information about deliveries, including information about the products being shipped, the route they are taking, and the status of the delivery. This could help increase transparency and accountability in the delivery process as well as reduce the risk of fraud and theft.

Internet of Things (IoT) devices, such as sensors and GPS trackers, may collect real-time data about delivery vehicles and their surroundings. This data could be analysed and used to optimise delivery routes in real time, as well as to track the location of deliveries and monitor the condition of the products being shipped.

Most of the processes that people use in their work lives can be represented as some object that goes through some flow. Each flow contains many stages, in each one of them the object can be manipulated by certain group of users.

In this article I want to suggest an approaches to effectively handle such flow based systems. 

Command-line interfaces (CLI) are a common way to use applications. In iOS, we usually use scripting languages like Bash or Ruby to build those CLIs and automate mundane tasks. The most popular CLI for app signing and build automation is, without a doubt, Fastlane, which was initially written in Ruby. Fastlane is a great tool, convenient and fairly easy to use, and a lot of effort came into building it.

However, there's a great chance you considered moving away from Fastlane to avoid learning Ruby and to lower the entry threshold for your developers. Setting up a Ruby environment could be quite tedious and require additional devs' expertise to write and support those scripts.

Explore how to build your own command line tools with Swift in this article.