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We are now publishing .NET Core container images to Microsoft Container Registry (MCR). We have also made other changes to the images we publish, described in this post.

Important: You will need to change FROM statements in Dockerfile files and docker pull commands as a result of these changes. 3.0 references need to be changed now. Most 1.x and 2.x usages can be changed over time. The new tag scheme is decribed in this post and are provided at the microsoft-dotnet-core repo, our new home on Docker Hub.

Summary of changes:

• .NET Core images are now published to Microsoft Container Registry.
• Updates will continue to be published to Docker Hub, for .NET Core 1.x and 2.x.
• .NET Core 3.0 will only be published to MCR.
• Nano Server 2016 images are no longer supported or published.

It's 2019! Everybody thinks they know code splitting. So - let's double check!

What does code splitting stand for?

In short – code splitting is just about not loading a whole thing. Then you are reading this page you don't have to load a whole site. When you are selecting a single row from a database – you don't have to take all.
Obvious? Code splitting is also quite obvious, just not about your data, but your code.

During load testing of Tantor Postgres databases or other PostgreSQL-based databases using the standard tool pgbench, specialists often encounter non-representative results and the need for repeated tests due to the fact that details of the environment (such as DBMS configuration, server characteristics, PostgreSQL versions) are not recorded. In this article we are going to review author's pg_perfbench, which is designed to address this issue. It ensures that scenarios are repeatable, prevents the loss of important data, and streamlines result comparison by registering all parameters in a single template. It also automatically launches pgbench with TPC-B load generation, collects all metadata on the testing environment, and generates a structured report.

If you’re like me and work with multiple AI coding agents, you know the frustration of managing different instruction files. It’s a pain to keep everything updated across various formats. But I’ve got some great news for you. A new, simplified standard has emerged, and it’s called AGENTS.md.

Complex systems often appear chaotic or incomprehensible, yet closer examination reveals that such complexity can frequently be reduced to a simple underlying mechanism. By systematically removing layers of emergent behavior, one can uncover a fundamental rule or equation from which the entire system originates.

For years, we’ve studied Oracle to make PostgreSQL a more migration-friendly option. We introduced tools similar to SQL profile and SQL plan baseline as AQO and sr_plan extensions. In some cases, PostgreSQL even outperforms Oracle, especially in automatic re-optimization.

Migrations from Oracle to PostgreSQL are usually smooth performance-wise, and we’ve even developed session variable extensions to make the transition easier. While many enterprise-only features exist, PostgreSQL often integrates popular solutions directly into the core.

In this tutorial, I’ll explain in simple terms what AI, AI agents, and workflows are, and then I’ll walk you through building your very first AI agent in Python using Google’s Agent Development Kit (ADK). By the end, you’ll understand the differences between these concepts and have a working content-assistant agent you can run from your terminal or a web interface.

Admit it, how many times have you wanted to quickly create an image for a post or presentation, but instead got stuck in an editor or endless searches for a suitable image on Google? Wouldn't it be great if the picture in your head could just appear instantly? Time is money, inspiration is on pause, and that's where AI comes to the rescue. Neural networks can generate anything you want, including the craziest ideas. No need to spend hours searching when, with a few clicks, you can see what was in your thoughts just a second ago.

By the way, notice the cover with the dinosaur? Let's call him Rex. Rex is himself a product of neural network creation. Today he'll be the main star of our experiments. But what will we do? Remember I mentioned crazy ideas? Well, to understand all the possibilities of generation, let's give AI a difficult task. We'll send Rex somewhere in space, for example to the Moon, let him put on a spacesuit and and have him grill some barbecue with Earth in the background. Interested? Then buckle up, we're heading into the world of image generation.

Historically, we’ve ended up using a few rather obscure PHP extensions—written and barely (if at all) maintained by their original authors—that aren’t available in standard Debian package sources.

We stick to the principle of “do it right, and it’ll work right,” which means Slackware-style dropping binaries into the system outside of package managers is frowned upon.

So instead, we’ll be building proper .deb packages for PHP extensions—without breaking compatibility with the existing environment.

A lot of people around me spend time trading on the stock market. Some trade crypto, some trade stocks, others trade currencies. Some call themselves investors, others call themselves traders. I often see random passersby in various cities and countries checking their trading terminals on their phones or laptops. And at night I sometimes write analytical or backtesting software—well, I did up until recently. All these people share a common faith and a set of misconceptions about the market.

We are a brokerage platform operating in a dynamic and complex domain. This specificity comes with a set of challenges. On the one hand, it entails a high variability of scenarios and potentially significant risks associated with errors. On the other hand, it has short development iterations with frequent delivery cycles. 

In this article, we will share how we maintain the quality of our numerous backend services, which provide essential information to our trading terminals.

Gen Z – a generation of young people born between the last few years of the 20th century and the first ten years of the 21st, are the primary users of a modern-day Internet. They started using technology since their early childhood, being almost constantly glued to their smartphones and tablets, which led to them being very demanding users that have their own requirements in UX. At the same time, they are known for their sense of humor and straightforwardness – which means they love using products that are both easy to navigate but full of interactive elements. Below we’ve collected a few things you should consider when creating a UX design aimed at a young audience.

In today's interconnected development world, secure authentication is not just a luxury—it's a necessity. Whether you're a seasoned DevOps engineer or a junior developer just starting your journey, understanding SSH key pairs is crucial for your daily workflow. They're the unsung heroes that keep our git pushes secure, our server access protected, and our deployments safe from prying eyes.

But let's be honest: SSH keys canbe confusing. With terms like “public key infrastructure,” “cryptographic algorithms,” and “key fingerprints” floating around, it's easy to feel overwhelmed. This guide aims to demystify SSH key pairs, breaking down complex concepts into digestible pieces that will help you make informed decisions about your security setup.

This article is the first in the series about the upcoming PostgreSQL 18 release. Let us take a look at the features introduced in the July CommitFest.

Planner: Hash Right Semi Join support
Planner: materializing an internal row set for parallel nested loop join
Planner support functions for generate_series
EXPLAIN (analyze): statistics for Parallel Bitmap Heap Scan node workers
Functions min and max for composite types
Parameter names for regexp* functions
Debug mode in pgbench
pg_get_backend_memory_contexts: column path instead of parent, new column type
Function pg_get_acl
pg_upgrade: pg_dump optimization
Predefined role pg_signal_autovacuum_worker

Firebase Performance offers a free, comprehensive solution for tracking app performance. As part of the Firebase suite, it provides seamless integration with other Firebase services like Crashlytics, making it easier to manage all performance and crash data in one place. This not only simplifies access but also streamlines team collaboration without the need to manage multiple platforms.

Recently I have bought starfive visionfive-2 SoC for my own experiments, honestly speaking
I am striving to work with risc-v. After some time I decided to share my experience. Here my bulletpoints:

Small preparation USB-to-Serial connector Write image to microSD/SSD Set boot mode settings

Boot Update bootloader Build kernel Native build Pod build Cross-build on amd64: fast and handy

Chroot to risc-v system from amd64 and install packages

Bonus 1: run qemu with risc-v

Bonus 2: build deb packages for risc-v

Bonus 3: kernel build script

Conclusions