Opinion
Original analysis by Zhemal Khamidun. Not AI rewrites — personal essays, opinions and deep dives.
Original Editorial Columns
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Why We Threw Out 70% of a Corporate Knowledge Base Without Writing a Single Line of Code: The Engineering Anatomy of Enterprise RAG
70–80% of enterprise RAG deployments fail because of garbage in the source documents: cleaning up a client's knowledge base from 12,000 to 3,800 current files…
The Klarna Lesson and 78,557 Laid Off in a Quarter: Why Rebuilding Teams With AI Beats Cutting Them
In Q1 2026, tech companies laid off 78,557 employees (48% of cuts attributed to AI automation), while in St. Petersburg the number of IT vacancies fell 41%…
Shadow AI in the Enterprise: Why 90% of Security Officers Break Their Own Bans and How a 3-Zone Traffic Light System Works
According to research from JumpCloud and Microsoft Work Lab, 80% of office workers already use neural networks at work, with 32% hiding it from management, and 90%…
Silent Failures in AI Pipelines: Why a Green Container Status Lies, and How We Cut LLM Costs by 90%
Why You Can't Trust an AI Agent to Grade Its Own Work: A Report Claiming -9,500 Lines When the Codebase Actually Grew by +21,489
An LMS and CRM at 205,000 Lines of Code, Built by AI Agents: The Real Cost of Running It, Three Lead States, and One \r Character
System Prompt Ablation: How I Shrank My Claude Code Config from 19,736 to 3,585 Lines Without Losing Quality
32.5 Million ₽ a Year for 300 People: How We Quantified the Real Economics of AI Adoption at a Company
Ninety-five percent of corporate AI rollouts produce no measurable financial impact because licenses are never tied to actual business processes.
Recursive Self-Improvement and Fable 5.1: Why the AI Race Moved Past AGI Debates Into Applied Orchestration
While the industry argues over AGI definitions, leading labs have closed the development loop, putting current models to work building the next generation of neural…
A Company's Immune System and 7,522 Hours Saved: What Actually Worked When Rolling Out AI at Alpina
16 Requests, 262 Checks, 6.5 Minutes: Three Autonomous AI Agents Solve One Business Task Live
Why 80% of Vibe-Coding Newcomers Get Stuck Before Writing a Single Line of Code — and How We Removed That Barrier
The 'Verify Before You Refuse' Rule: How to Stop an AI Agent From Giving Up at the First Error
Reverse-engineering expensive websites with AI: why half of the web's 'complex 3D' can be assembled in two evenings
Impressive promo sites with complex lighting and volumetric graphics used to require a month and a half of design studio work plus a dedicated WebGL developer.
929 Automated Tasks and 152 Homemade Tools: How Non-Programmers Reclaimed 83% of Wasted Time
Every Friday, assembling marketing reports, spreadsheets, and presentations by hand eats up to half the workday for specialists.
Hamidun News AI Agenda Index: First Half of 2026
7,734 pieces from 91 sources in six months. In headlines, AI agents (892) outpaced OpenAI (768) and Anthropic (740). The first edition of the Hamidun News AI agenda…
Head-to-Head AI Architecture Breakdowns
ChatGPT vs Claude
ChatGPT by OpenAI and Claude by Anthropic are the two leading general-purpose AI assistants. ChatGPT wins on breadth: voice, images, video and the largest app ecosystem. Claude …
ChatGPT vs DeepSeek
ChatGPT is the Western flagship; DeepSeek is the leading open challenger from China, famous for near-frontier models at a fraction of the cost. ChatGPT wins on product completen…
Claude vs Gemini
Claude by Anthropic and Gemini by Google are two flagship assistants with different strengths. Gemini leads on multimodality, a huge context window and deep Google integration. …
Llama vs Mistral
Llama by Meta and Mistral are the two pillars of open-weight AI — American and European. Llama offers the biggest community and range of sizes; Mistral counters with efficient c…
Flagship Enterprise AI Case Studies
AlpinaGPT: Enterprise Multi-Model AI Platform and RAG Built on a Verified Business Library
Deploying AlpinaGPT across enterprise teams cut the time required to draft analytical briefs, training curricula, commercial proposals, and meeting minutes by 45–60%, while migr…
Sberbank: AI resolves 65% of customer inquiries in the contact center
As of Q1 2026, AI resolves over 65% of customer inquiries: 66% in voice channels and 71% in chats. The market comparison sets the frame: per Frank RG's 2026 estimate, the indust…
Avito: Proprietary A-Vibe LLM and VLMs Automate Moderation and Sales Across 200M+ Listings
Over 98% of all marketplace listings are now moderated automatically without human intervention, while contextual chat analysis significantly reduced multi-step phishing incidents.
Yandex Go & Lavka: Graph Neural Networks and Demand ML Orchestrate Millions of Rides and Dark Stores
Global RL-assisted dispatch and chained-trip matching reduced driver deadhead mileage and average passenger wait times by 15–22%, increasing driver hourly utilization without ra…
T-Bank: AI protection against phone scammers saved customers 1.2 billion rubles
The financial trajectory of 'Protect or Refund' has been published in stages: in its first six months it saved customers 170 million rubles; by May 2025 the cumulative amount re…
X5 Group: AI Dynamic Pricing, Demand Forecasting, and Computer Vision Across 25,000+ Stores
Embedding AI across supply chain replenishment, dynamic pricing, and in-store operations generates tens of billions of rubles in verified annual EBITDA contribution for X5 Group.
Test Frontier LLMs & Autonomous Agents in One Workspace
Run Claude Opus 5.5, GPT-6, Gemini 3.1, and DeepSeek side-by-side in Nomad AI (nomadnet.ai) — unified model routing, deep research, and multimodal workflows under one subscription.