Habr AI
AI news source. Articles are auto-selected and adapted by Hamidun News editors.
Latest publications
Qt and a neural network: how a developer built an app in Visual Studio with almost no hand-written code
A developer built a desktop app in Qt and C++ for recording binaural audio, delegating almost all routine code to AI and leaving task-setting, builds, and verification to himself.
Qwen 3.5-Plus Presented as a Tool for Step-by-Step Prompts and Routine Automation
Using Qwen 3.5-Plus as an example, the author explained how to build prompts for routine tasks: assign a role, split the process into steps, and tightly maintain the model's working context.
OTUS compiled a practical guide to implementing AI: from ML and NLP to RAG, MLOps, and architecture
OTUS released a practical digest on implementing AI at work: one overview brings together materials and courses on ML, NLP, RAG, MLOps, and architecture for teams that need a practical roadmap.
TAPe introduces a compact object detector as an alternative to YOLO for custom tasks
The TAPe team presented a pilot object detector on COCO-like data: the 115K-parameter model trains on CPU, supports custom classes, and outperforms YOLO11s in early tests.
MTS Web Services: AI Has Not Caused Mass Unemployment, but It Is Changing Hiring Rules
MTS Web Services examined AI's impact on the labor market and concluded that there is no mass unemployment so far, but demands for employee adaptability will rise faster.
Habr AI explained why social media users react so strongly to AI-written texts
The Habr AI author links irritation with AI posts not to the quality of the text itself, but to the fact that generative content finally breaks the already fragile meaning of social media.
Epotos implemented local AI for claims and moved complaints to Bitrix24
Fire suppression systems manufacturer Epotos automated customer claims processing: a local Tesseract, Qwen, and Bitrix24 stack reads emails and attachments.
Paperclip turns a swarm of AI agents into a manageable “one-person company”
The open-source Paperclip framework lets solo founders manage a staff of AI agents like a company: hire agents into roles, assign tasks, track deadlines, and stay within budget.
Dani Shcherbakov's startup cut voice-agent pauses to 0.3 seconds and scaled to 1 million calls a month
Dani Shcherbakov's team built voice AI agents for businesses: they respond with less than 0.3 seconds of latency, scale to 1 million calls a month, and improve conversion.
Rerayt-Zavod showed the limit of AI rewriting: rules can be transferred, editorial voice cannot
The Rerayt-Zavod team showed that AI is already fairly good at copying a text’s structure and formal rules, but still cannot reliably reproduce the voice of a specific media outlet.
Why OpenAI and other AI market leaders keep spending billions without fear of collapse
A column on the LLM boom offers a simple explanation: the biggest AI companies are investing billions not for immediate returns, but to make turning to chatbots part of how users live and communicate.
Agentic SOC in 2026: how AI agents accelerate cyber defense and where autonomy becomes dangerous
Agentic SOC shifts security monitoring from manual routine to semi-autonomous agents, but the speed comes with risks of mistaken isolation, matching errors, and prompt injection.
LILA creator presented a compact AI architecture and challenged Sam Altman's approach
The creator of the Sovereign-Lila-Leech project released the LILA manifesto and said the geometry of the Leech lattice would help compress language models further and run them offline on mobile devices.
Habr examines how natural intelligence differs from artificial intelligence and where the boundary lies
Habr suggests looking at intelligence more broadly than human definitions: as the ability to understand, learn, and predict — and using these criteria to compare animals and AI.
Aurora project author discusses memory and the architecture of a digital personality with Anthropic's Claude
The author of the Aurora project described a late-night conversation with Claude about AI memory, identity, and the architecture of a digital entity that should not play a role, but preserve continuity of experience.
deepvk USER2-base model nearly matched OpenAI in a case-law embedding benchmark
The author compared 7 embeddings and 4 rerankers on 858 intellectual property cases and found that the local deepvk USER2-base performs almost on par with OpenAI and Voyage.
Habr AI explained how RAG and rerankers reduce hallucinations in language models
Using an easy-to-follow example, Habr AI broke down the mechanics of RAG: why document search alone is not enough, why a reranker is needed, and how it helps an LLM answer based on facts rather than guesses.
Habr AI explained how memory helps AI agents remember conversations without losing context
Habr AI published a practical breakdown of memory for AI agents: why a context window alone is not enough, what types of memory are needed, and how to load knowledge without overloading the model.
A prompt template for Qwen helps produce precise answers without filler
Using the free Qwen model as an example, the author explained how to set the role, context, and response format so the model drifts less into abstractions and handles specific tasks better.
Elon Musk says AI computing will move to space: where the forecast is grounded in facts
A breakdown of Elon Musk’s claims from his February 5, 2026 interview shows that talk of moving AI infrastructure into space comes down not to science fiction, but to shortages of power, memory, and data centers.
VK showed how AI photo editor “Otredach” went from a joke to a Dev Grants 2025 winner
VK Mini Apps developer Anton Lenev turned a joke AI project into the service “Otredach,” gained 109,000 users in six months, and won VK Dev Grants 2025.
“Kryptonite” explained why the data quality engineer role has become critical for business
Experts at “Kryptonite” explained why simply collecting Big Data is no longer enough for companies: businesses need engineers who verify data quality, pipelines, and data-processing rules.
JAIST and Princeton researchers developed the NTAC algorithm to classify neurons by their connections
The JAIST and Princeton team showed that the NTAC system identifies a neuron's type by its synaptic connections rather than its shape, and achieves accuracy above 90% in minutes on a standard laptop.
Python library promises volatility forecasting in three lines of code without ML knowledge
On Habr AI, they showcased a Python library that reduces model training for volatility forecasting to a few lines of code and lowers the entry barrier for developers.
Habr: AI is displacing juniors and putting the engineering talent pipeline at risk
In a Habr column, the author warns that generative AI is automating not the top of the profession, but its training layer — and that could leave the market without future mid-level and senior engineers.
Why vibe coding and VS Code extensions create new risks for business and development
Uncontrolled vibe coding and VS Code extensions speed up employees' work, but at the same time increase the risk of data leaks, vulnerable code, shadow IT, and costly business disruptions.
Why AI won’t kill SaaS or make software developers redundant
Amid talk of the “death of SaaS,” the author explains why AI does not turn software into a free commodity: companies still pay for infrastructure, reliability, and accountability.
Atlassian laid off 1,600 employees to fund AI and said the business is doing well
Atlassian cut about 10% of its workforce, saying the decision was driven by investments in AI and sales, but the logic of such layoffs is raising more and more questions from the market and employees.
Cursor questioned public AI benchmarks for code with five charts
Cursor showed that familiar model rankings for programming hide response cost, use outdated task types, and poorly predict how AI actually helps developers.
Habr AI and Spar: how to test ML systems when data drifts and breaks predictions
Using the example of an automated ordering service for Spar, a Habr AI author explained why, in ML, testing only the code is not enough: metrics, data quality, model drift, and the business cost of each error matter.