From One Local Stack to Three Training Profiles
How a local testing stack evolved from one Docker-based environment into three distinct runtime profiles with clearer intent: lightweight, full, and server.
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LLMs vs AI Agents: A Practical Mental Model for Developers
A practical mental model for developers: understand LLMs, AI agents, function calling, and why coding agents built on the same model behave differently.
Read morePlaywright CLI, Skills and Isolated Agentic Testing
A practical guide to Playwright CLI for AI agents: run browser actions from the terminal, load Playwright Skills, mock APIs, capture traces, and build isolated agentic tests.
Read moreThe Current State of Agentic Software Development
A pragmatic analysis of agentic software development in 2026: real productivity gains, enterprise constraints, code review challenges, and why architecture, testing, and process discipline matter more than ever.
Read moreAI 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.
Read moreWhy 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 moreLearning 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 moreAgentic 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 moreBuilding RAG with Gemini File Search
Learn how to build and test a practical RAG workflow with Gemini File Search, including managed retrieval, grounding checks, and implementation trade-offs.
Read moreTesting 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 moreTest-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 moreUnderstanding 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 moreAI + 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 moreMermaid diagrams: When AI Meets Documentation
Learn how Mermaid diagrams improve technical documentation and AI workflows by expressing architecture as code that stays readable, maintainable, and current.
Read morePlaywright Agentic Coding Tips
Practical guidance for using agentic AI with Playwright to design, generate, and validate UI and API tests with better speed, quality, and reliability.
Read moreAI Tooling for Developers Landscape
A practical landscape of AI tooling for developers across IDE copilots, project starters, CLI assistants, and autonomous agents with trade-offs.
Read moreHow 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 moreHow I use AI
A detailed walkthrough of how I use AI daily for coding, learning, writing, research, and content creation, including prompts, tools, and workflows.
Read moreAI 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 moreFrom 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 moreThe 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.
Read moreLearning Tips for Using ChatGPT in Software Testing
Essential tips for software testers using ChatGPT effectively, covering data privacy, bias awareness, prompt engineering, and decision-making strategies with AI assistance.
Read moreRevolutionizing Test Engineering with ChatGPT
Explore how ChatGPT transforms test engineering with practical examples of test generation, CI configuration, tool comparison, and creative testing scenarios.
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