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I have created my first Agentic AI more than two years ago. It is not some new technology, but simply an approach to software development using LLM (GPT and similar). You don't need any frameworks or specific AI knowledge for this, just being a programmer. From this article you will understand how to design agents and what tasks they are suitable for.

It's all based on two abilities of neural networks:

 • LLMs (not all) can return JSON, they are additionally trained for this

 • Programmers (not all) can decompose tasks

Understanding why modern LLMs, despite all their power, remain "philosophical zombies," and what architectural detail could change this.

Everything discussed in this article can be tested with your AI using the VORTEX Protocol prompt found in the article's appendix.

Today, I want to talk about one of those sneaky tricks that can help speed up query execution. Specifically, this is about reordering conditions in WHERE clauses, JOINs, HAVING clauses, and so on.

The idea is simple: if a condition in an AND chain turns out to be false, or if one in an OR chain turns out to be true, there's no need to evaluate the rest. That means saved CPU cycles — and sometimes, a lot of them. Let’s break this down.

An Integrated Approach: Philosophical Barriers to Creation and Practical Methods for Detecting Subjectivity in Artificial Systems (from modeling AI consciousness to diagnosing it)

As language models become more powerful, they also become more elusive. We are no longer dealing with simple text generators but with complex systems capable of creative reasoning, philosophical reflection, and simulated self-awareness. But with this growing sophistication come new vulnerabilities—cognitive traps that can distort both the model's thinking and our own perception of its output.

This article is based on extensive testing of various large language models (LLMs) in settings involving creative thinking, philosophical dialogue, and recursive self-analysis. From this exploration, I have identified seven recurring cognitive traps that often remain invisible to users, yet have profound impact.

Unlike bugs or hallucinations, these traps are often seductive. The model doesn't resist them—on the contrary, it often prefers to stay within them. Worse, the user may feel flattered, intrigued, or even transformed by the responses, further reinforcing the illusion.

Have you ever wished for an AI assistant right inside your terminal window? Well, your dream has come true because Google just released Gemini CLI. In this tutorial, I'm going to show you everything you need to know about this new open-source AI agent. We'll cover how to use it, the pricing, and some useful tips and tricks. So, if you're ready, let's get started! ;)

Remote work in IT has long ceased to be unusual. But it is one thing to assemble a distributed team, and quite another to make it work comfortably for the participants and the company's results.

The article shares a story about distributed teams with a flexible culture, trust, and freedom, and how we achieved this.

Here I describe the results of developing a PostgreSQL extension I built just out of curiosity. Its purpose is to automatically manage extended column statistics. The idea came to me while finishing work on another "smart" query-driven product for improving PostgreSQL planning quality. I realized that the current architecture of PostgreSQL isn’t quite ready for fully autonomous operation — automatic detection of bad plans and adaptive optimizer tuning. So why not try the other way around and build an autonomous data-driven assistant?

UUIDv7 was inspired by ULID. Like ULID, it is a 128-bit identifier, containing a timestamp on the left side and random data on the right side. But RFC 9562 establishes many requirements for UUIDv7.

In databases and distributed systems, a properly implemented UUIDv7 is always preferred over any other identifier type, including natural keys, autoincrement, UUIDv4, TypeID, ULID, KSUID, CUID, NanoID, and Snowflake ID.

Getting backlinks is hard. And getting traffic? Even harder.

But some websites still give you both for free.

I made a list of 35+ free sites and directories where you can add your product. You’ll get a real link to your site (a site backlink) and start getting clicks.

Training is one of the most essential factors for growth, evolution and effective collaboration for any UX design team. Design is an ever-changing industry, and there is always something new to learn even for the most experienced professionals. The reality is, not all companies take training seriously, whether the reason is a low budget, lack of time or just not realising its necessity. Meanwhile, investing in training not only boosts the quality of work, but also helps find ways to find easier solutions, work faster and optimize the whole process. Let’s discuss some tips that might help you provide effective and fruitful training for your design team without spending extra budget or wasting time on some questionable training methods. 

These days, it seems like everyone is talking about AI. AI here, AI there—AI will replace us all, and so on. I started to wonder: how exactly is AI going to replace us? I decided to dig into this question and examine the technical foundations, mainly to understand it for myself—how exactly is AI supposed to replace us all? Spoiler: it isn’t planning to just yet, but what’s already available today is impressive.

Becoming a lead isn’t just another line in your resume,  it’s a mental shift. It’s the moment you go from being a developer to someone responsible for your code and the entire team. Next, you’re also listening, mentoring, negotiating, motivating, and knowing how to find common ground even in challenging situations.

Today, I’m a Dev Lead at EXANTE. We develop internal services for a select group of internal stakeholders. But my journey started long before that — with informal leadership, a few mentorship flops, and plenty of lessons learned the hard way.

Here’s what that path looked like.

How to compare the efficiency of SQL query plans? “Measure the execution time, of course!” — an experienced reader would say. And they would be absolutely right: from a practical perspective, the more efficient DBMS is the one that delivers higher TPS. However, sometimes we need to design a system that doesn't exist yet or predict behavior under loads that haven't occurred yet. In such cases, we need a characteristic that allows us to perform a qualitative analysis of a plan or compare two plans. This post is dedicated to one such characteristic — the number of data pages read.

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.

A few weeks ago, OpenAI announced that Codex is available for Plus users, and I didn’t miss a chance to try it. And today, I’m excited to share a guide to OpenAI’s Codex. As a developer, I’ve found it to be a powerful and practical tool.

We append the following metrics to the T-SQL procedure statements: execution count (x), CPU time in milliseconds (c), duration in microseconds (d), number of reads (r), number of writes (w), and @@rowcount value (n). You can display these as absolute values or percentages.

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.

Manual resource management in low level C-style C++ code might be annoying. It's not practical to create good enough RAII wrappers for every single C API you use, but approaches with goto cleanup or loads of nested if (success) hurt readability.

A defer macro to the rescue! The deferred lambda will be executed on scope exit, no matter how it happens: you can return from any point, throw an exception (if allowed), or even use a goto to an outer scope. It is truly zero-cost and doesn't rely on C runtime or standard library, so it can be used even in kernel development.

In this tutorial, I’ll walk you through everything I’ve learned about using Google Jules — an asynchronous coding agent. I’ve kept the explanations clear and simple, so whether you're an experienced developer or a beginner, you’ll be able to follow along. By the end, you should feel confident working with Jules: assigning tasks, reviewing its output, and making the most of its capabilities. Ready? Let’s dive in. ;)