Artificial Intelligence

How a Product Owner at Exante uses AI to cut complex feature delivery time in half — without replacing human input in product decisions.

I ran into a very practical problem after doing a lot of local work with AI agents. I had Codex projects, Claude Code projects, regular repositories edited with agents, drafts, pipelines, instructions, skills, artifacts, and several machines. At some point it became hard to tell where the current version of a project lived, which files were safe to push, where agent instructions belonged, and where source code had already been mixed with logs and intermediate output.

That is why I built AI Workspace System: a small set of shell scripts, conventions, and Markdown documentation that makes local AI-agent work predictable. It is not an IDE and not an agent orchestrator. It is a thin infrastructure layer around Git, GitHub, Codex, and Claude Code.

The core idea is simple: all projects should be visible from one list, instructions should follow one structure, sync should be safe by default, and machine-specific details should not live in the repository.

When you buy a $20 ChatGPT subscription, you get access to about $1,000 worth of tokens. When you buy a $100 Claude subscription, you get access to about $2,000 worth of tokens. However, these subscriptions cannot be connected to Cursor directly. The API and subscription request formats are different, so you need a workaround — proxying the requests.

AI coding assistants are becoming better every day. But most still work one prompt at a time. You ask something, get an answer, and then guide the next step manually.

Renowned biologist Richard Dawkins recently published an essay exploring the possibility of LLM consciousness following a two‑day conversation with Claude AI.

Let“s first look at why an essay by this particular author caused such a stir in scientific circles, while thousands of ordinary users fail to turn heads when they claim their AI companions are sentient. The latter constantly post endless walls of text from their chats with LLMs, where the density of words like ‘consciousness,’ ‘soul,’ ‘reflection,’ ‘recursion,’ ‘emptiness,’ ‘warmth,’ ‘love,’ and ‘pain’ exceeds all reasonable limits. It is worth noting that the semantic density of these dialogues is practically zero‑but we will return to that later.

In early 2026, one new law and one far‑reaching legislative initiative are expected to seriously affect digital freedoms in the EU. The first allows police to collect biometric data and target individuals; the second aims to put all metadata into one box and then use AI to run investigations. Naturally, both laws were adopted under the mantra of protecting democratic values, rights, and freedoms. Xeovo has examined the sprawling regulatory texts and explains what exactly Members of the European Parliament are aiming at.

Like millions of others convinced they possess knowledge the world desperately needs to hear, I decided to write a book on prompting. In the process (which, by the way, turned out to be far more difficult than anticipated), I found myself examining LLM clichés. You know the ones. At least, in the comment sections of tech blogs, hundreds of self-proclaimed experts use them to spot AI-generated text.

Anyway, these clichés definitely exist, and many authors now routinely add blocklists of these phrases to their prompts to weed them out. Whether this is actually a good or a bad thing is what I’ll break down below.

Google recently released the Agentic CLI, a powerful tool that makes building, testing, and deploying AI agents faster and more intuitive. I think it’s super useful, so I’ll walk you through the entire lifecycle of an AI agent using the CLI.

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.”

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.