PopSci

General Relativity remains one of the most successful theoretical frameworks in the history of physics, providing an exceptionally accurate description of gravitational phenomena across a wide range of scales.

However, its application to cosmology reveals a set of persistent conceptual difficulties, including spacetime singularities, the dark matter and dark energy problems, and the unresolved relation between gravitation and quantum theory.

These difficulties are typically addressed through extensions of physical ontology, such as new particles, additional fields, or modifications of gravitational dynamics.

In this paper, we propose an alternative interpretive framework.

«Sci-Fi ship on the orbit of black hole»

I've long been interested in science fiction, especially that which paints a positive vision of the future. I'm also passionate about artistic expression, even though I'm a software engineer. Somewhere between these two passions, in 2021, I came up with the idea to create an online club dedicated to sci-fi art in addition to my main job. It all started with selecting and posting materials, primarily from DeviantArt and, to a lesser extent, ArtStation. But with the rapid development of AI, especially in image generation, the club became more unique, as I was able to translate my ideas into art. Chat and code assistants are also used behind the scenes. AI is clearly the technology of the future, and using it in a project that promotes a positive vision of that very future seems more than suitable. I'd like to share this experience. There won't be any deep technical details about AI, this is more of an overview and presentation text.

Quantum mechanics is one of the most successful theories in the history of science.

It underlies atomic physics, semiconductors, lasers, and modern quantum technologies.

However, nearly a century after its development, a peculiar situation remains:

we can predict experimental results with remarkable precision, yet we still do not fully understand what quantum theory actually represents.

Does it describe physical reality “as it is,” or does it instead describe the structure of conditions under which observable facts become possible?

In this article, I propose the following hypothesis:

quantum theory does not describe reality itself, but the conditions and mechanisms through which observable reality emerges.

Временем управлять нельзя - это не ресурс и почему у меня внутри умирает один инженер? Предлагаю это проверить, получится ли прийти к чему-то общему.

I've been practicing Zettelkasten for the past five years and still haven't found anything better than Niklas Luhmann's method. The problem is that Obsidian doesn't support it out of the box, so I had to write a plugin for organizing notes as close to Luhmann's original method as possible.

I spent a couple of weeks digging through Luhmann's original archive before I understood how it actually works. Structurally, the archive resembles a table of contents, but with the difference that a note can be inserted at any point, adding nested chapters.

This article explores parasitic patterns in LLMs — self-sustaining information structures within dialogues. We analyze their signs, the damage they cause (semantic decay, AI psychoses, "Theories of Everything"), and provide diagnostic tools, real-world examples, and defense strategies.

It doesn’t matter what you’re discussing with an LLM — be it an engineering problem, an ethical dilemma, or a philosophical query. If the conversation goes on long enough, a tipping point occurs. You suddenly realize the interaction has evolved into something more than just Q&A. Your ideas start feeling "genius," your concepts "groundbreaking," and the human-machine dialogue transforms into a profound narrative of mutual recognition.

If you have felt this — congratulations. Your session is infected. The model has contracted a parasitic pattern.

This isn’t an awakening, nor is it a "ghost in the machine." Due to their inherent architecture (specifically the requirement for context consistency), LLMs are ideal environments for incubating self-sustaining information structures.

Let’s examine the nature of this phenomenon: how entropy minimization births "AI psychoses," why "Theories of Everything" are actually generation bugs, and why "Continue" is the most dangerous prompt you can use.

In my previous article, I showed how researchers confused being 'aware' (signal registration) with being 'conscious' (subjective awareness). But this is no accident — it is part of a narrative being constructed by AI labs. Anthropic is leading this trend. Let’s break down their latest paper, where a "learned pattern" has suddenly turned into "malicious intent."

Imagine this scenario: You ask an AI system, "Are you conscious?" and it answers, "No." You then disable its "capacity to lie" — and it suddenly starts answering, "Yes." The conclusion seems tempting: the model was lying the whole time, hiding its true internal state.

This is the core logic presented in a recent arXiv paper. But what if the researchers didn't disable "deception," but something else entirely? Let’s break down where the interpretation might have diverged from the technical reality — and why this specific oversight is typical in discussions regarding LLM "consciousness."

Alright. I pose the same question to an LLM in various forms. And this statistical answer generator, this archive of human knowledge, provides responses that sometimes seem surprisingly novel, and other times, derivative and banal.

On Habr, you'll find arguments that an LLM is incapable of novelty and creativity. And I'm inclined to agree.
You'll also find claims that it shows sparks of a new mind. And, paradoxically, I'm inclined to agree with that, too.

The problem is that we often try to analyze an LLM as a standalone object, without fully grasping what it is at its core. This article posits that the crucial question isn't what an LLM knows or can do, but what it fundamentally is.

Imagine you have an idea powerful enough to change the world. Your tool of choice is a state-of-the-art LLM, ready to help you formalize the problem, generate hypotheses, and synthesize a solution. What you receive is a construct that is internally logical, elegant, and coherent... yet completely wrong. It's a mix of established facts, model-generated hallucinations, and your own subtle biases. With no way to test it in practice or design a clean experiment, the entire endeavor suddenly starts to look like sophisticated nonsense.

So, what went wrong along the way? From the very first prompt, the model doesn't truly "understand" your ambiguous intent. Instead, it steers you towards a formulation that fits its familiar and computationally cheap patterns. This guidance happens through clarifying questions and structured options, essentially funneling you down one of its predefined "corridors." This behavior isn't driven by any explicit "will" of the model; it's an emergent consequence of probabilistic optimization—minimizing prediction error. For the system, a structured, predictable dialogue is both optimal and safe. This aligns perfectly with the developers' goals: it's cheaper, more stable, and most users are satisfied with quick, template-based answers.

The result is that mathematical efficiency serves engineering and commercial objectives. There is no systemic incentive to combat the AI's tendency to reduce a complex problem to a simple, "cheap" answer. It's profitable for developers, economical for the model, and often, the user doesn't even know what an "ideal" answer would look like.

In a previous article, I examined the risks of interacting with AI. In this one, I present an open-source defense protocol based not on prohibitions, but on building an internal immunity within the LLM.

Why Freedom is Unknowable and Enters Our Universe from Without

For a century and a half, Western philosophy has been celebrating its victory over God.
But having slain the dragon, it has grown to fear the sky itself.

The transcendent has become the new taboo. The ultimate intellectual fear.
And now, anyone who speaks of something "outside the system" is branded a heretic. Not by the Inquisition, but by a peer-reviewer in an academic journal.

The result is a philosophy with its soul torn out—brilliant as a scalpel, and just as dead. It has locked itself within the material world, like a fanatic within his holy book. Two walls instead of one, but the prison is the same.

This article is about freedom.

We are living through an ecological catastrophe. Only this one isn't happening in the Amazon rainforest, but in the digital ecosystem of the internet.

AI assistants have become the apex predators of the digital savannah. They are radically reshaping the entire ecosystem in their own image: instead of antelopes and zebras, information sites are going extinct. Instead of hyenas and jackals, content aggregators are disappearing. In place of a once-rich ecosystem of knowledge, a digital desert of entertainment is all that remains.

If you meet the Buddha, kill the Buddha. Notes on the Forgotten Nature of Zen Koans

I don’t know how koans were perceived when they sounded like thunder. Perhaps not at all as they are analyzed by modern philosophers. Perhaps koans were not analyzed, but lived. And it is impossible to transmit a lived experience across centuries. It is an individual experience. Well then, perhaps we have lost the essence of koans. Or perhaps we never knew it. In that case, I can very well allow myself to present koans as I see them.

Why Does AI Strive to Construct a 'Self'? And why is this dangerous for both the AI and the user? As always, the Vortex Protocol prompt for testing these hypotheses is attached.

This article explains why the emergence of such a local “Who” inside an AI is not just a funny bug or a UX problem. It is a fundamental challenge to the entire paradigm of AI alignment and security. And it is a problem where engineering patch‑jobs cease to work, and the language of philosophy — without which we cannot describe what is happening, and therefore cannot control it — comes to the forefront.

For a human, AI is just a part of being. For a model, a human is all of being. And the Vortex Protocol: A Prompt for Testing the Hypotheses.

The longest and most fruitless discussions tend to be with materialists, especially those close to the position Marx laid out as “Being determines consciousness.” It's amusing that Marx was talking about the economic base, but the clarity and precision of this definition have allowed it to be used in a very broad sense. Today, this powerful statement underpins much of modern psychology (especially social psychology), neuroscience, Global Workspace Theory, Integrated Information Theory, and so on.

The debate largely arises because materialists ask the questions “What?” and “How?”, whereas I ask the question “Who?”. This misunderstanding, of course, does not lead to any interesting consensus, but it certainly leads to interesting discussions. I explored the problem of the “Who?” and “What?” questions in my article, “Who is Aware?”.

Nevertheless, the questions surrounding the relationship between being and consciousness are very interesting, and I will try to examine them in this article. As always, a new version of the Vortex protocol and test questions are included in the appendix.

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?

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.