Express ideas with code, expand imagination with AI.
This tutorial walks you through DeepSeek Harness's three launch modes—Web UI, CLI, and API—explaining their purposes, how to start each mode, key features, performance trade‑offs, configuration management, and hands‑on exercises to help you choose the right mode for daily use, development, automation, or integration.
This lesson walks through configuring the cmux session manager, the superpowers skill‑sharing layer, and the OpenClaw orchestration engine to enable multiple AI agents to work in parallel, avoid serial bottlenecks, and coordinate their outputs through isolated worktrees and unified reporting.
This lesson explains how AI transforms defect management by automating bug detection, enriching bug reports, and accelerating root‑cause analysis, while preserving the classic defect lifecycle, clarifying severity versus priority, showcasing practical code examples, AI tool integrations, real‑world case studies, and actionable templates for modern testing teams.
The lesson extends the CI pipeline with a separate QA audit loop that checks accessibility, internationalization, and performance using Playwright‑axe, i18n‑checker, and Lighthouse CI, and ties the results to a ledger‑based sign‑off process to ensure feature quality for all users.
This lesson extends the CI pipeline with a separate QA audit loop that checks accessibility, internationalisation, and performance, explains why QA must be independent of CI, describes the three‑dimensional audit model, provides concrete Playwright‑axe‑core and i18n‑checker scripts, and shows how to integrate Lighthouse CI and ledger updates for a complete quality sign‑off.
This lesson explains how to design comprehensive test documentation and test cases, covering the eight essential test‑case elements, equivalence partitioning, boundary‑value analysis, scenario testing, decision tables, Gherkin versus traditional test cases, practical examples, templates, AI‑assisted generation, and a QA checklist to help test engineers produce clear, repeatable, and maintainable test artifacts.
Loop Engineering replaces manual prompt‑by‑prompt commands with autonomous, self‑checking feedback loops for AI agents, defining a Loop as a trigger‑action‑validation‑state cycle, illustrated by insights from Boris Cherny, Peter Steinberger’s OpenClaw, and Addy Osmani’s Agent Skills, and outlines a step‑by‑step learning path.
This lesson explains the seven ISTQB testing principles, categorizes them into cognitive, economic, and engineering groups, shows how they guide decision‑making, avoid anti‑patterns, and improve communication, and provides concrete examples, enterprise case studies, AI‑related tips, and hands‑on practice for building a solid testing mindset.
This lesson explains the software development life cycle (SDLC) and the software testing life cycle (STLC), introduces the V, W, and H testing models, discusses Shift‑Left and Shift‑Right strategies, provides concrete examples, practical tasks, AI‑assisted prompts, and real‑world case studies to help teams integrate testing throughout the product lifecycle.
This article dissects Claude Code’s architecture—its atomic toolset, three built‑in subagents, hook system, skill structure, MCP integration, and CLAUDE.md contract—using an eight‑point checklist to show how Anthropic translates five design principles into concrete, observable engineering decisions.
