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Static analyzers' primary aim is to search for errors missed by developers. Recently, the PVS-Studio team again found an interesting example proving the power of static analysis.

Four months of awesome work together with a few new contributors finally result in a new major release, which I'm happy to announce about.

Now we get completely new matrices, improved parser, a lot of new functions, almost rewritten interactive package (for working in Jupyter) and many more.

This article about a big update in a FOSS symbolic algebra library for .NET, I hope it may be interesting for someone!

When creating a .NET library with a partially platform-specific API, we should think about how to pack it into a NuGet package so that the installed library will work in various scenarios (.NET Framework, .NET Core, self-contained application and so on). Unfortunately, it is difficult to find a step-by-step instruction on the web that describes how to perform this task. This article is intended to be such an instruction.

Network architecture at hyperscalers is a subject to constant innovation and is ever evolving to meet the demand. Network operators are constantly experimenting with solutions and finding new ways to keep it reliable and cost effective. Hyperscalers are periodically publishing their findings and innovations in a variety of scientific and technical groups.

The purpose of this article is to summarize the information about how hyperscalers design and manage networks. The goal here is to help connecting the dots, dissect and digest the data from a variety of sources including my personal experience working with hyperscalers.

DISCLAIMER: All information in this article is acquired from public resources. This article contains my own opinion which might not match and does not represent the opinion of my employer.

We at Data Science Digest have always strived to ignite the fire of knowledge in the AI community. We’re proud to have helped thousands of people to learn something new and give you the tools to push ahead. And we’ve not been standing still, either.

Please meet Data Phoenix, a Data Science Digest rebranded and risen anew from our own flame. Our mission is to help everyone interested in Data Science and AI/ML to expand the frontiers of knowledge. More news, more updates, and webinars(!) are coming. Stay tuned!

The new issue of the new Data Phoenix Digest is here! AI that helps write code, EU’s ban on biometric surveillance, genetic algorithms for NLP, multivariate probabilistic regression with NGBoosting, alias-free GAN, MLOps toys, and more…

If you’re more used to getting updates every day, subscribe to our Telegram channel or follow us on social media: Twitter, Facebook.

Microservices Architecture is a well-known pattern for building a complex system that consists of loosely coupled modules. It provides better scalability, and it is easier to develop a system in multiple teams so that they don’t interfere with each other too much. However, it is important to choose the right way of communication between the services. Otherwise, this kind of architecture can do more harm than good.

Visual Studio from Microsoft has long been the main development environment to work with the PVS-Studio analyzer. Our analyzer started off on Windows, so Visual Studio was an obvious and reasonable choice. In more than 10 years of PVS-Studio development, the analyzer has become available for several other languages and platforms. No wonder people ask us if we can integrate PVS-Studio into their favorite IDEs.

When a company decides to build a marketplace, one of the most critical issues to solve is building interaction between all operational processes.

You need a system where you can manage and exchange customer, partner, and order data. Often the back-office of a marketplace consists of a complex of systems. However, we have been working with marketplaces for many years and have come to the conclusion that the integration between back-office systems should be seamless. At least between 6 fundamental parts. Thus, we have created the Scallium platform, where the business manages the processes of the marketplace.

The popularity of online education is increasing every month. And since there is an increase in popularity, there is an increase in competition and an improvement in quality: many authors of online schools realized that nowadays it's not enough to just make something mediocre using the frontal camera of the iPhone 4. You need to work on the quality of the picture, sound, presentation of material, etc. Not only did the pandemic prove that you can teach salsa and even crochet online, so it also gave an excellent push to improve the materials taught + diversity in the labor market. Moms on maternity leave can rejoice - now you can truly earn a hundred thousand million per second, sitting at home.

During the lockdown, which is now weakening, then becoming more active with a vengeance, a huge number of new professions have appeared. Proficients in creating involving Instagram Stories, course producers, e-learning specialists and other unknown-before-creatures - all of them have mastered a new job that gave them the holy bread in the pandemic. Once you hire a creator of involving Instagram Stories (and let's all hope this person was teached by a professional, not a snake oil salesman), your business grows just with adding some creativity in advertising that will take the heart of even the most fastidious customer. Same works with e-learning specialists: you recruit them and they do all the important tasks from writing articles that will also help in promoting your classes to arranging lessons on your Coursera course. A complete flight of imagination: if it seems to you that some jobs don't exist, then you just don't spend enough time online. In general, online work has become popularized as never and now hardly anyone will say: "He is engaged in some kind of garbage - he makes TikToks but it would be better for him to toil at the factory!". In total, online work has become popularized as never and now hardly anyone will say: "He is engaged in some kind of garbage - he makes TikToks, but it would be better for him to toil at the factory!".

PVS-Studio is a static analysis tool that helps find errors in software source code. This time PVS-Studio looked for bugs in Storm Engine's source code.

The new issue of DataScienceDigest is here!

The impact of NLP and the growing budgets to drive AI transformations. How Airbnb standardized metric computation at scale. Cross-Validation, MASA-SR, AgileGAN, EfficientNetV2, and more.

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Hackathons could be very intimidating and stressful. The key to getting better is doing simple projects. In this article, we will look at an example of a web app that can be used for sharpening your skills when you prepare for a hackathon. We will use a powerful Google API based on Machine Learning and apply the following technologies: ASP.NET, HTML, Docker, Heroku, and Git.

One of the main challenges in the world of web development is to achieve a seamless user experience. Many factors come into play when trying to achieve the goal of a smooth UX. The user interface, the load-time of webpages, and updating any data/input sent, are some prominent examples.

All web development frameworks offer tools that enable the developer to make the UX as smooth as possible. After all, it’s an important aspect of a website. In the case of the popular front-end framework Angular, such a tool is the RxJS library.

Through RxJS, Angular applications can employ the reactive programming and functional programming paradigms to ensure a smoothly working inside-out. This article acts as a quick guide to RxJS and how it is used in Angular applications. It will discuss some of its robust features and give you their flavor through code examples. Using RxJS to speed up and improve an Angular application’s UX is common practice and this article will show you why.

What Does RxJS Do Exactly?

In the everyday workings of a website, it handles different requests and events. Requests like inputting/outputting certain data or transforming some data, and events like responding to a mouse click or keyboard key press. If all of the requests and events are coming in one after another (synchronously), they are responded to in that order. However, few frameworks can respond quickly and reactively in the case of asynchronous requests and events.

RxJS is a JavaScript library of functions that allow to smoothly handle asynchronous events and requests. The name comes from the fact that it enables reactive programming in JavaScript. It allows for playing with and tracking time in your Angular application. The amount of time between responses or events can be fine-tuned as required through RxJS, letting the developer control the responsiveness of the application.

Originally posted on DEV.to

In my previous post, I touched on the point that Redis is more than just an in-memory cache.

Most people do not even consider Redis as a primary database. There are a lot of use cases where Redis is a perfect choice for non-cache related tasks.

In this article, I will demonstrate how I built a fully functional Q&A board for asking and upvoting the most interesting questions. Redis will be used as a primary database.

I will use Gatsby (React), Netlify serverless functions and Upstash Serverless Redis.

Upstash has been a good choice so far and I decided to try it out in a more serious project. I love everything serverless and how it makes things simpler for me.

Serverless will be a great choice for most tasks however you need to know the pros and cons of the tech you are using. I encourage you to learn more about serverless to get the most out of it.

The software development world is a dynamic one. Tools and applications are constantly upgrading and getting better to make advanced technology possible. Technologies that do not upgrade will eventually become obsolete and fade away with time. The principle of constant upgrading is especially true for the really popular tools, like the front-end framework Angular. To provide developers with a more robust and solid front-end development experience, Angular introduced a cutting-edge compilation and rendering pipeline in their version 9. The new engine was christened Ivy.

With Ivy, Angular aimed to double down on the advantages it traditionally offered. This article will take you through all the new advancements that come bundled with Ivy. It will also quickly go through what is deprecated and how it handles Angular applications made on the previous View engine. Angular Ivy was made to accommodate all exciting new developments that could be needed in the future. This article talks about the updates that followed the original rollout.

What’s New In Angular Ivy

Ivy was introduced as the default engine with Angular’s version 9 and continues to support subsequent versions. One of the main aims of Angular’s Ivy engine was to amplify the performance and minimize the file size of Angular applications. The development team behind Angular employed different techniques to achieve the aim of faster and lighter Angular applications. The prominent strategies behind Ivy are discussed below:

The Ivy Compiler

The developer or owner of a software product often faces the question of choosing a suitable location for hosting server capacity. As you know, software always meets hardware.

We don't often get the chance to write something on parallel programming issues. This time we "got lucky". The TraceEvent standard method has some implementation peculiarities. They resulted in an error with multiple threads blocking. So we'd like to warn users about this nuance and cover this interesting case from our users support practice. Why was our support involved? Keep reading to find out. Enjoy the reading!

It is an efficient searching technique. Searching is a widespread operation on any data structure. Hashing is used to search specific records from a large domain of records. If we can efficiently search a record out of  many records, we easily perform different operations on that data. Hashing is storing and retrieving data from the database in the order of O(1) time. We can also call it the mapping technique because we try to map smaller values into larger values by using hashing.

Following are the significant terminologies related to hashing

Search Key

In the database, we usually perform searching with the help of some keys. These keys are called search keys. If we take the student data, then we search by some registration number. This registration number is a search key. We have to put the search keys in hash tables.

Hash Table

It is a data structure that provides us the methodology to store data properly. A hash table is shown below. It is similar to an array. And we have indexes in it also like an array.

Searching is the method to search for a specific element in a group of an element. It needs a unique identity that is associated with the desired element. As a unique identity of that desired element has been found, the index to that desired element is returned.  The index indicates the location or address where that specific element has been found in the list elements of the array. If the desired data is found, particular data has been returned. Otherwise, it returned a null value.

There are several categories of search algorithms such as.