Artificial Intelligence

In 1976, Richard Dawkins introduced the concept of the meme in The Selfish Gene—a unit of cultural information that behaves like a gene: it copies itself, mutates, and undergoes selection. The idea proved so infectious that it became a meme itself: it entered science, spilled over into popular culture, morphed into internet folklore, and... got stuck.

I propose patching memetics via an IT metaphor. A meme is not a virus. A meme is mere data. The actual virus is the Narrative—the executable code of culture.

Key takeaways:

The human as a server, not a user: We are hosting providers for ideas.

Emotion is the spike protein of the narrative virus.

The user is a biological USB flash drive for AI.

A meme is a corpse. A narrative is a zombie.

Consciousness is a narrative that evolved into an Operating System.

Marketers are in a state of panic. SEO is "dead," link-through rates are plummeting, and digital promotion seems futile as LLMs dominate user attention. Naturally, a wave of experts has emerged, offering advice on how businesses can get "noticed" by AI. And, like clockwork, "GEO" (Generative Engine Optimization) services have flooded the market.

In this article, I will explain why SEO isn't going anywhere and why most current theories on GEO are fundamentally flawed.

In this article, I’ll show you how to unlock the full power of GitHub Copilot agents inside VS Code. There are actually three main types of Copilot agents-most people only know about one, but I’m going to show you all of them. By the end, you’ll be able to create custom agents that act as your own specialized “sub-agents.”

It's a strange question to ask in 2026, when agents are at the top of most companies' agendas and calling yourself "AI-native" has become almost mandatory. Against that backdrop, process excellence sounds like a phrase from another era, when any system rollout was preceded by a separate phase: processes were described, aligned on, and prioritized, and only then would anyone decide what to actually automate.

And yet, the closer you look at how companies are currently deploying AI, the more you get the sense that we might be about to rediscover something we quietly skipped.

In the previous article I described my “anime factory” in detail — a pipeline that automatically turns episodes into finished Shorts. But inside that system there is one especially important module that deserves a separate deep dive: a virtual camera for automatic reframing.

In this article, I will break down not just an “auto-crop function,” but a full virtual camera algorithm for vertical video. This is exactly the kind of task that looks simple at first glance: you have a horizontal video, you need to turn it into 9:16, keep a person in frame, and avoid making the result look like a jittery autofocus camera from the early 2010s.

But as soon as you try to build it not for a demo, but for a real pipeline, engineering problems immediately show up:

Hi, Habr!

Over the past few months, I have been building a system that I internally call an “anime factory”: it takes a source episode as input and produces a ready-to-publish YouTube Short with dynamic reframing, subtitles, post-processing, and metadata.

What makes it interesting is not just the fact that editing can be automated, but that a significant part of this work can be decomposed into engineering stages: transcription, audio and scene analysis, strong-moment discovery, “virtual camera” control, and a feedback loop based on performance metrics.

In this article, I will show how this pipeline is structured, why I chose a modular architecture instead of an end-to-end black box, where the system broke, and which decisions eventually made it actually usable.

GitHub Copilot CLI brings Copilot directly into your terminal. You can ask questions, understand a project, write and debug code, review changes, and interact with GitHub without leaving the command line.

Every week, I watch tutorials, save articles, bookmark tools, and collect ideas I want to come back to later. But a few days later, the problem shows up: I may remember the topic, but not the details. I know I saw something useful, but I cannot explain it clearly or apply it with confidence.

In this article, I would like to share my experience participating in the Agentic Legal RAG Challenge 2026 hackathon. Our team is called "Sparks of intelligence".
Original article in Russian: https://habr.com/ru/articles/1014520/

AI video generators in 2026 allow anyone to create high-quality videos from text prompts, images, or scripts in just minutes. 

This guide explains how the technology works, compares the leading tools on the market, and highlights their strengths, limitations, and best use cases to help you choose the right solution for your creative or business needs.

After the unexpected divorce between LeCun and Meta, there is a lot of talk that the dead-end in LLM progress will be overcome through the physics of the world. That is, having a neural network work with physical data from the surrounding environment will allow the model to acquire meaning and an understanding of its actions. LeCun has a foundational paper that nobody is going to read. So, I'll summarize it as best I can. Essentially, the idea is that the current trajectory of LLM development is doomed. As long as they are predicting the next token, real understanding — the emergence of real meaning — is impossible. LeCun proposes training neural networks on physical world data, assuming that building a model of it will allow the system to discard details and focus on meaning.

I agree with LeCun that using world data will partially solve the data scarcity problem. But here I see a problem that engineers might not understand. A physical model of the world is actually much poorer than human knowledge. Newton described the entire infinite number of possible falls with a few lines of formulas. I doubt LeCun wants to spend billions of dollars on this wonderful deduction.

Imagine having a personal AI agent running on your computer. It can read files, run commands, automate tasks, and remember your workflows. In this guide, you will learn how to run OpenClaw with Ollama locally and choose the best local LLM models.

LLMs fail at elementary math. Corporations spend billions, but ultimately are forced to attach calculators to computing machines of incredible power. All attempts to fix this via Chain-of-Thought, fine-tuning on arithmetic tasks, or context expansion have failed.

I conducted a series of experiments to understand why, and came to the conclusion that neural networks are simply not meant for discrete arithmetic. Their true purpose is continuous transformations.

This article describes the implementation of a novel neural network architecture that combines the precision of symbolic AI with the generalization capabilities of LLMs. As always, experiments and code are included.

Accessing Claude from Russia might seem like a daunting task due to the service's regional restrictions. In this article, I will explain in detail how to register for the web version and API of Claude, what tools are needed to bypass the restrictions, and how to use the service safely in the future. This guide is based on personal experience and includes registration methods that are current as of late 2024 and have been tested in practice.

After searching Russian-language resources, I realized that there is very little practically useful information, or it might not account for changes in the service's operation. Ordinary users have to gather information piece by piece - from various YouTube videos, forums, or superficial articles.

Therefore, I decided to create an up-to-date guide based on my personal experience and knowledge, taking into account all the pitfalls that an inexperienced Claude user might encounter.

DeepSeek is increasingly unavailable due to server overload. In this article, we will solve the problem in an original way – by installing Deepseek locally so that it works without any internet connection at all.

A quick guide on how to enable the new Google AI Mode and set it as the default search in Chrome. Now, queries from the address bar open directly in “AI Mode”.

Hi, I'm Dima and, like many, I've been using neural networks to create images for many years. But over the last year and a half, the market for generative models has changed so much and become so huge that it's impossible to try all the models. However, the question of which neural network is the best still remains. Benchmarks are poorly suited for a real assessment of creativity and cost, so I've gathered 10 models in one place, what they are best suited for, and what the price is (or rather, the free limit). I hope this helps you choose the most optimal neural network for your tasks.

Below is an honest rating based on quality, realism, convenience, and price.

In this article, we take a detailed look at Ollama — a tool for running large language models (LLMs) locally. You will learn how to install the program, choose a suitable model, understand formats and quantization, configure the system for your hardware, and work both via the CLI, and via the API. Practical tips, configuration examples, and VRAM recommendations will help you use Ollama as effectively as possible for dialogues, text generation, code, and other tasks.

In this article, we will not only install a local (and free) alternative to ChatGPT, but also review several open LLMs, delve into the advanced settings of LM Studio, connect the chatbot to Visual Studio Code, and teach it to assist us with programming. We will also look at how to fine-tune the model's behavior using system prompts.

I'll tell you about the ways you can install the Chinese model locally and use it without the internet. And do it in a way that you work not through the console, but through a user-friendly chatbot interface.