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.
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! ;)
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.
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.
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. ;)
CAPTCHA has become a familiar part of the internet: distorted texts, “find all the traffic lights” images, audio riddles, and other challenges that distinguish humans from machines. Every bot-system developer or QA engineer automating web scenarios has at least once run into a script suddenly stumbling over a CAPTCHA. A natural question arises: can a program be taught to solve CAPTCHAs the way a human does—quickly and reliably? In this article I will try to figure out how AI CAPTCHA solvers are built, from classical OCR methods to modern neural networks.
This article presents a business-driven approach to software architecture, focusing on maximizing profitability through technical decisions. It advocates for:
Distributed, stateless services with immutable models for scalability
Isolated third-party integrations without disrupting core logic
Simple, modular design
Best for:
✔ Tech leads designing scalable systems
✔ Managers optimizing dev efficiency
✔ Stakeholders evaluating ROI on architecture
I recently got my hands on Windsurf AI, and I wanted to share my experience with this AI-first Integrated Development Environment (IDE). If you’re a developer like me, always on the lookout for tools to boost productivity, this might be on your radar. So, is Windsurf AI the real deal? Let’s find out.
I’ve recently been playing with Agent Mode in VS Code, which looks promising. If you’re using VS Code and want to give your development a turbo boost, you’ll want to hear about this.
We at Verilog Meetup constructed an exam/interview problem that has an interesting property: if a student tries to figure out a solution by thinking by himself, he usually succeeds; however if he dumps the problem on ChatGPT, the solution fails (does not pass the automated test), and the student goes into a death spiral of futility, kicking ChatGPT to get the solution right.
There is nothing weird about the problem, we do this in the industry all the time:
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.
The whimsical image of a chess knight in profile among the stars is perhaps the most recognizable among deep space objects. It can be said that the Horsehead Nebula tops the recognition rankings among nebulae and galaxies. Perhaps the Andromeda and Orion Nebulae resonate more with the general public, but only specialists and advanced amateurs know what they look like. Even among them, there are often cases when the famous Andromeda Galaxy is confused with another galaxy, even by experienced popularizers of astronomy. The Orion Nebula, too, can look different in pictures taken with various filters. Yet, the profile of the chess knight allows every inhabitant of Earth to recognize that in front of them is the one-of-a-kind and completely unique Horsehead Nebula.
Let's begin by noting that when we talk about the Horsehead Nebula, we refer to two fundamentally different formations in this region of our Galaxy. Only together do they create this memorable visual image. Moreover, what we see—the glowing diffuse background—is not the Horsehead Nebula. It is a dark silhouette against a light background—a bizarrely shaped dust cloud opaque to the visible radiation of stars and nebulae. If there were no relatively bright emission nebula behind it, we might not even know about any dark gas-dust cosmic horse.
The background for the recognizable horse profile is created by the hydrogen nebula IC 434, discovered by William Herschel in the late 18th century. However, Herschel did not notice any amusing details in the outline of this nebula, although he was an excellent observer—much more keen-sighted and attentive than most of his followers. There are also objective reasons for this—telescopes in Herschel's era were imperfect, and their dark metal mirrors lost from 50% to 80% of the light that entered them.
The large but sparsely populated constellation Monoceros, barely visible to the naked eye, can take pride in hosting an extraordinary treasure: the Rosette Nebula. Monoceros is a relatively recent addition to the star map. Lacking bright stars, astronomers of the pre-telescope era deemed this region unworthy of a constellation. After all, how could a constellation exist without luminous stars? A few 4th-magnitude stars hardly suffice to form a recognizable figure. Consequently, old star maps depicted nothing within the Winter Triangle — formed by Betelgeuse, Sirius, and Procyon (between the constellations Orion, Canis Major, and Canis Minor. Only with the telescope’s invention did it become clear that this region contained notable objects, necessitating a name. Johannes Hevelius, creator of a stunning star atlas, coined the name in the early 18th century. Thus, astronomers gradually accepted a mythical, horse-like beast with a sharp horn — located where the third eye might be — among the winter constellations.
The term "Carina Nebula" is sometimes translated as "Nebula of Carina," evoking the image of a beautiful female name. However, this is a misinterpretation. "Carina" is Latin for "keel," referring to the keel of a ship—not just any ship, but the Argo, depicted on ancient star maps. Over time, the Argo constellation was divided into several modern constellations: Carina, Puppis, Vela, and Pyxis (Compass). The stars of the Argo are invisible from mid-northern latitudes. Even in the best seasons (winter and spring), Carina, Puppis, and Vela remain below the horizon. They can be observed from the northern tropics and farther south. However, in ancient times, when Greek astronomers imagined the Argo in the sky, the Earth's rotational axis was positioned differently due to precession. This allowed the Argo to be visible from Mediterranean shores. Precession, a gradual shift in the Earth's axis, completes a full cycle every 26,000 years. Over three millennia, the stars of the Argo gradually disappeared from the northern sky.
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.
I recently tried using Firebase Studio, and it has been an interesting experience that I want to share with you. It's a free, browser-based tool from Google that allows you to build full-stack web apps with AI assistance. Want to know more? Then read this article until the end.
Armenia is a lucky country. Back in the 1990s somebody lobbied Synopsys, the #1 leader in the Electronic Design Automation market, to create a division there. It was joined by Mentor Graphics / Siemens EDA, another EDA leader, then NVidia. Synopsys Armenia became the largest Synopsys division outside the US Silicon Valley and Boston, although the Taiwanese may tell you otherwise. Since Synopsys and Mentor make software used by chip designers in Apple, Samsung, AMD and all other electronic companies, Armenia has an unfair advantage over all their neighbors in Caucasus and Central Asia.
In addition, Armenia has friendly relations with Russia, and most Armenians speak Russian as well. This has facilitated the move of many Russian companies to Armenia, for example, a RISC-V semiconductor IP provider Syntacore. Finally, as we can see from the recent conference EDA Connect, Armenia is attracting the attention of electronic and EDA companies from China.
EDA Connect featured not only academic and industrial papers but also a hackathon on FPGA design, attended by local students from Yerevan State University, the American University of Armenia, the Russian-Armenian University, the French University in Armenia and others.
Since sharing my initial thoughts on Google's Gemini 2.5 Pro, I've dug even deeper and gathered answers to many common questions developers like us might have. This AI model is making waves, so let's break down everything you need to know – from its groundbreaking features to the practical stuff like cost and limitations.
