Habr AI
AI news source. Articles are auto-selected and adapted by Hamidun News editors.
Latest publications
X5 Tech added AI skills assessment to developer interviews
X5 Tech has integrated an AI block into developer interviews and evaluates not whether tools are banned, but how a candidate formulates prompts, checks the model’s answers, and takes responsibility for the code.
Why AI makes errors in requirements and architecture more costly despite speeding up development
AI has accelerated the path from idea to prototype, but that has made mistakes in requirements and architecture more costly: the wrong decision now scales before anyone can stop it.
Habr AI: why creating sentient AI may be more dangerous than enhancing the human brain
In a Habr AI column, the author argues that developing sentient AI carries greater ethical and existential risks than neural interfaces and enhancing humans themselves.
Anthropic, OpenAI, Google, and Microsoft have launched an AI platform war instead of a model race
The AI market is shifting from a model race to a platform battle: it now matters to control not only the models themselves, but also agent execution, data, security, and workflows.
OpenGrall introduces “Engineer” mode: robot writes drivers and configures modules on its own
OpenGrall has added an “Engineer” mode: via text command, the robot can create plugins, write code for new hardware, and calibrate modules, but the final integration decision remains with a human.
SD Studio turns local Stable Diffusion into “its own Midjourney” with an LLM assistant
SD Studio combines local Stable Diffusion, an LLM, and ready-made presets into a single pipeline to produce illustrations faster on your own GPU and avoid overpaying for external services.
Habr explained how to protect smart home voice control from leaks and hacks
Habr explained why accurate recognition alone is not enough for a smart home: without encryption, speaker verification, auditing, and local processing, voice commands are easy to intercept or spoof.
BentoML showed how to turn Grounding DINO into a production service with a web API
Using Grounding DINO as an example, the author showed how BentoML packages a computer vision model into a service with parameter validation, Swagger UI, a Docker build, and ready-to-use endpoints.
Habr details an AI framework for Claude with Clean Architecture and a TDD cycle
An author on Habr presented a framework that guides Claude through stories, test plans, and quality gates to produce code in the style of Clean Architecture and TDD instead of chaotic vibe coding.
Falcon Tech explained how machine vision for cities grew into a network of 4,000 systems
Falcon Tech described how, over eight years, it turned parking monitoring into a large-scale video analytics platform: in Moscow, it already runs on more than 4,000 hardware-software systems.
Falcon Tech showed how Moscow's video monitoring system grew out of parking enforcement
Falcon Tech explained how it built a video monitoring system for Moscow: from parking enforcement to analyzing the load on urban infrastructure using data from thousands of hardware-software systems.
MTS: the first architectural decisions in AI made today set constraints for decades ahead
In a column on Habr AI, the MTS team compares AI development to multilayered technical debt: decisions in code, data, and processes made now will constrain products for many years.
red_mad_robot engineer showed how to build an NER service for résumés: from annotation to API
A practical breakdown of NER for résumés was published on Habr: the author shows how to define entities, build and annotate a dataset, compare BERT models, and package the result in FastAPI.
Mo Gawdat of Google X: why AI's main threat is not code, but ethics
Former Google X executive Mo Gawdat believes AI already steers our attention, will accelerate a painful reshaping of the labor market, and will run into humanity's main limit — a lack of ethics.
How AI is changing SOC architecture: why correlation rules are no longer enough
A SOC can no longer rely only on correlation rules: modern attacks are quieter, longer, and more complex, so AI is increasingly taking over noise filtering and context gathering.
Telegram chats became a stable lead generation channel with a 5,000-ruble AI bot
An agency built an AI bot that monitors open business chats on Telegram, filters spam, and finds client requests, generating 15–22 qualified leads a month at a cost of about 5,000 rubles.
SberZdorovye: neural network non-determinism is a pipeline failure, not a model property
A SberZdorovye architect argues that with fixed data and environment, a neural network must produce a single result, and discrepancies usually point to errors in the code, hardware, or pipeline.
Nano Banana, Qwen, and ChatGPT compared on image generation quality
In a new comparison of image generators, four models, including Nano Banana, Qwen, and ChatGPT, were tested on the same prompts to see which handles real visual tasks better.
Habr AI: why LLM hallucinations look less like a mathematical bug and more like a human lapse
Habr AI has published a column arguing that LLM hallucinations should be viewed not only as an engineering defect, but also as a repetition of typical human reasoning failures.
Qdrant and Hybrid RAG: corporate document search without the cloud or leaks
Hybrid RAG combines semantic and exact search so companies can find answers in archives of PDFs, scans, and spreadsheets locally, without sending data to the cloud.
Directum proposed workflow agents as a practical path for introducing AI into business processes
Directum described a workflow agent model for business processes: AI operates under predefined rules within the corporate environment and is already cutting contract review time from 30 to 5 minutes.
Data Science in digital manufacturing: how enterprises collect data and reduce defects
Data Science turns fragmented manufacturing data into a tool for quality control, predictive maintenance, and process optimization across the product's digital thread.
Claude and the illusion of honesty: why people trust chatbots more than their own judgment
A column about Claude and “absolutely honest” prompts shows how easily people mistake a bot’s confident tone for expertise and begin delegating their own judgment to the machine.
Wildberries explained how to train AI agents through reflection, interviews, and a God-agent
A Wildberries engineer described a set of practices for working with AI agents: splitting context, conducting an interview before a task, reflecting after execution, and using a separate God-agent to tune the system.
Google NotebookLM can be turned into a personal mentor on any topic in five minutes
The guide shows how to build a personal mentor in Google NotebookLM from books, articles, and videos to get advice on your own tasks and learn faster from verified sources.
Evan Armstrong: why the context layer is changing the economics of enterprise software and hiring
Technology analyst Evan Armstrong argues that with the arrival of AI agents, value is shifting from standard SaaS applications to the context layer, where a company’s processes, access rights, and logic are stored.
Putin orders a state plan for AI as Russian business shuts down 90% of pilots
Russia is hardening its AI strategy: authorities are preparing a national plan and rules for neural networks, regions are scaling health services, and 90% of corporate pilots still never make it to production.
Why AI tools speed up code delivery but also inflate bugs and technical debt
Teams are increasingly getting a rapid boost in code output from AI, but with it come more defects, longer review queues, and new technical debt that can easily slip into production.
SimpleOne explained why vibe coding speeds up releases but hurts code and code review
SimpleOne broke down the benefits and risks of vibe coding: AI speeds up product releases and hypothesis testing, but also brings technical debt, harder code review, and dependence on external models.
Habr AI suggested using news sentiment as a trading signal for the crypto market
Habr AI described an approach in which shifts in sentiment across news and social media are used as a trading signal for the crypto market instead of classic indicators tied to price history.