AI Testing Skills: The Evolution Beyond RAG and MCP
Learn what AI testing skills are and how they differ from RAG, MCP, and tool calling. A practical guide to building efficient AI testing agents with reusable playbooks.
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Why AI Coding Is Moving Back to the Terminal (CLI Agents)
Why AI coding is moving back to the terminal. A practical look at AI CLI agents and terminal-based AI coding assistants, multi-repo workflows, microservices, architecture-first thinking, and verification-driven development.
Read articleLearning AI
Learn AI faster with three paths: theory-first student, power-user workflows, and builder tooling. Understand LLM fundamentals, prompting, agents, and how to connect them.
Read articleAgentic Testing - The New Testing Approach
Discover agentic testing: how AI agents can test applications through white-box code analysis and black-box exploration using Playwright MCP, Chrome DevTools MCP, and terminal tools. Learn practical examples with Java Spring Boot and React, explore benefits and challenges, and see how coding agents are becoming testing agents.
Read articleBuilding RAG with Gemini File Search
Learn how to build Retrieval-Augmented Generation (RAG) with Gemini File Search.
Read articleTesting LLM-based Systems
Learn practical strategies to test and validate Large Language Model (LLM) systems. Discover how to ensure reliability, evaluate AI outputs, and maintain quality in real-world LLM-powered applications.
Read articleTest-Driven AI Development (TDAID)
Discover Test-Driven AI Development (TDAID) — a modern approach that merges Test-Driven Development (TDD) with AI-powered software engineering. Learn how to apply TDD principles to AI coding agents, build reliable feedback loops, and prevent regressions in non-deterministic systems. This guide explains why TDD is making a comeback in the AI era, how to structure agentic workflows around tests, and what practices help teams deliver high-quality, maintainable code with AI tools like Claude, Cursor, and Gemini.
Read articleUnderstanding Playwright Agents
A deep dive into Playwright Agents and the Model Context Protocol (MCP) — how Microsoft’s latest AI-powered Playwright release automates test planning, script generation, and self-healing browser tests across Chrome, Firefox, and WebKit.
Read articleAI + Chrome DevTools MCP: Trace, Analyse, Fix Performance
How DevTools MCP enables AI agents to record real performance traces (LCP/CLS/TBT), analyse them, and apply fixes—bringing Lighthouse-style audits into an iterative debugging session. Notes on INP (field) vs TBT (lab) included.
Read articleMermaid diagrams: When AI Meets Documentation
How to use Mermaid diagrams to create human and AI-friendly architecture as code.
Read articlePlaywright Agentic Coding Tips
Playwright Agentic Coding Tips for writing/generating API and UI tests.
Read articleAI Tooling for Developers Landscape
A landscape of AI tooling for developers.
Read articleHow does Playwright MCP work?
Learn how Playwright MCP combines AI agents, browser automation, and the Model Context Protocol (MCP) to enable intelligent testing, debugging, and documentation. Explore how it works, its architecture, and real-world use cases.
Read articleHow I use AI
How I use AI to code, learn, write and generate content.
Read articleAI vibe coding notes from the basement
Reflections on "vibe coding", context windows, tips, and the real costs of AI-driven development, plus a live case-study building a websocket traffic monitor with Cursor and Sonnet agents.
Read articleFrom Live Suggestions to Agents: Exploring AI-Powered IDEs
A comprehensive guide to evaluating AI-powered IDEs based on live code suggestions, LLM chat integration, RAG performance, and agent capabilities for enhanced development workflows.
Read articleThe rise of AI Driven Development
Personal insights on effectively using AI tools for software development, highlighting practical tips for prompt engineering, understanding LLM limitations, and leveraging AI-powered coding assistants.
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