- AI Day: how translating a long sentence grew into ChatGPTEnglish | Русский AI Day: how translating a long sentence grew into ChatGPT Twelve years ago a paper came out about machine translation. Step by step, ChatGPT grew out of it. September 1 — the day the school year traditionally starts, Knowledge Day in Russia and much of the post-Soviet world. It also makes a … Continue reading “AI Day: how translating a long sentence grew into ChatGPT”
- The Seven Biggest Claude Skills Collections: What’s Inside, Which to Trust, and How to Use ThemEnglish | Русский The Seven Biggest Claude Skills Collections: What’s Inside, Which to Trust, and How to Use Them TL;DR — if you only read this box: Start here: anthropics/skills for a small, production-grade, vetted set. Add glebis/claude-skills (~90 tidy personal skills) if you want more, or obra/superpowers if you want a whole workflow, not … Continue reading “The Seven Biggest Claude Skills Collections: What’s Inside, Which to Trust, and How to Use Them”
- Language, format, placement: how to write prompts an LLM understands betterEnglish | Русский Language, format, placement: how to write prompts an LLM understands better I spend my days wrangling big prompts to language models — and the same question keeps coming up: what actually works better? Is it true that if you write tersely, telegraph-style, with no filler, the model both understands more precisely and … Continue reading “Language, format, placement: how to write prompts an LLM understands better”
- How to Organize a Repository for an LLM AgentEnglish | Русский How to Organize a Repository for an LLM Agent More complex is better? I don’t think so. Five tiers of repository organization: Tier 0 — flat files. Everything in context. Prototypes, configs, small projects under 20 files. Tier 1 — text search + CLAUDE.md. How every AI coding agent works. Code projects … Continue reading “How to Organize a Repository for an LLM Agent”
- The AI Productivity Paradox and Trend: Why Experts Slow Down but it still profitable, or not?English | Русский The AI Productivity Paradox: Why Experts Slow Down Why experts get slower, novices get faster, and context matters more than profession The Paradox Nobody Expected Experienced developers with five years of tenure, working on repositories exceeding one million lines of code, gained access to cutting-edge AI tools. Economists predicted they would speed … Continue reading “The AI Productivity Paradox and Trend: Why Experts Slow Down but it still profitable, or not?”