Opinion
Original analysis by Zhemal Khamidun. Not AI rewrites — personal essays, opinions and deep dives.
Original Editorial Columns
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I Interrogated 6 AI Models About Myself Through a Procurement Manager's Eyes: Amazon, Esoterica, 'Telo Hudelo' and 5 Holes We Closed in a Day
A ~/.claude Architecture With 200+ Skills: Keeping the System Prompt Under 50K Tokens and Paying $0.15 Per Session
The $448 Billion Paradox: Why 95% of Corporate AI Pilots Deliver Zero P&L Impact
From Prompting to Context Engineering: 6 Working Techniques That Save Hours of Fine-Tuning Answers
When tokenized by modern language models, Cyrillic text costs 2–3 times more than English in token count and eats through the working attention window faster.
4.5x Faster Product Launches and 87% Budget Savings: 3 Secure AI Architectures Under Russia's 152-FZ
According to McKinsey, 88% of companies already use AI in at least one function, but only 1% have reached industrial-grade maturity due to information security…
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…
Vector Search vs. Knowledge Graph: What It Really Costs to Build and Maintain a GraphRAG
The AI development paradox: why greenfield coding speeds up 55% while mature codebases slow down 19%
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
Two linked production systems — an educational platform LMS (121,853 lines of TS/TSX, 47 routers, 288 procedures) and a CRM with a unified registry of 334 tools…
System Prompt Ablation: How I Shrank My Claude Code Config from 19,736 to 3,585 Lines Without Losing Quality
When you move to a new generation of models, old rules baked into the system prompt turn into a wasteful context tax and keep the agent from thinking for itself.
Recursive Self-Improvement and Fable 5.1: Why the AI Race Moved Past AGI Debates Into Applied Orchestration
250 subagents vs. 4 threads: why orchestration architecture matters more than a loud model name
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
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
929 Automated Tasks and 152 Homemade Tools: How Non-Programmers Reclaimed 83% of Wasted Time
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.