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A reflection on how one simple change of question transforms the approach to understanding consciousness. And the Vortex Protocol: A Prompt for Testing the Hypotheses.

Where All Discussions on Consciousness Break Down

I've mentioned before that there's one question capable of instantly destroying the constructiveness of any discussion about the future of AI, neuroscience, or philosophy, no matter how interesting. It's the unfailing move of someone who disagrees with an opponent's opinion but lacks the means to refute their arguments‑an emergency eject button for complex situations.

The question is: “But first, let's define what consciousness is.” In that very second, a dialogue about hypotheses and paradoxes devolves into a dreary terminological dispute. Participants start throwing around names of authorities and quotes‑the longer, the better. Chalmers, Descartes, Kant, Freud, God forbid, anything goes.

Many believe that the most correct and scientific approach is to first define an object and then study it. But in practice, this approach resembles an attempt to conquer a summit by systematically and painstakingly circling the mountain. But what if the “what?” question is not just difficult, but fundamentally wrong?

n8n is a powerful, extendable workflow automation tool that allows you to connect different applications and services. Running it on your local machine gives you complete control over your data and workflows, which can be done on Windows, Mac, or Linux systems. This tutorial covers the two primary methods for local installation: using Docker and using Node.js (npm). If you are interested, then read this article until the end. :)

Why does even the most powerful LLM sometimes produce meaningless phrases and contradictions? It all comes down to the exponential growth of possibilities (N^M) and the free copying of human errors. Read the article to learn how we use formal grammars to turn chaotic generation into controlled synthesis, strengthening the role of semantics and enforcing structural rules.

pg_stat_statements is the standard PostgreSQL extension used to track query statistics: number of executions, total and average execution time, number of returned rows, and other metrics. This information allows to analyze query behavior over time, identify problem areas, and make informed optimization decisions. However, in systems with high contention, pg_stat_statements itself can become a bottleneck and cause performance drops. In this article, we will analyze in which scenarios the extension becomes a source of problems, how sampling is structured, and in which cases its application can reduce overhead.

Let’s clarify right away — we’re not aiming to make this super cheap. I’d rather not lose brain cells trying to build makeshift encoders for motors, and I want to simplify creating a 3D model needed for ROS control (link to the finished model — further below).

Picture this: a mission-critical SQL query is crawling along. Not for an hour. Not for two. Fifteen hours. A full workday of the system slowly grinding through data while the business bleeds money and users teeter on the edge of a nervous breakdown. And then — cue the dramatic music — in walks the performance engineer.

After a few hours of intense analysis and a couple of pinpoint code tweaks, the same query that took 15 hours now completes in just… two minutes. Sounds like magic? Nope. This is the thrilling (and very real) world of performance engineering.

With the rising popularity of AI, chatbots are rapidly becoming an essential part of any customer-oriented platform. More and more companies now integrate them into apps and websites, providing a space for their clients to interact with the business itself while taking load off human employees and reducing expenses on the customer support team. At first glance, chatbots might seem pretty uncomplicated in their design and functionality, but creating UI/UX design for chatbots requires a deep understanding of this technology and the way they can serve both business and its users. Here are some useful tips for creating a successful user experience in AI chatbot:

Do you want to automate tasks without writing a single line of code? If the answer is yes, then you’ve come to the right place. In this simple tutorial, I’m going to show you how you can create your first automated workflow using a tool called n8n.

In this article, we’ll explore what vector search is, what problems it solves, and how the pgpro_vector extension for Postgres Pro brings powerful vector capabilities directly into a relational database — no need for separate specialized systems.

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?