OpenSpec is a CLI tool that introduces a spec‑driven workflow between developers and AI assistants, outlining clear "what" and "how" stages, change proposal management, integration with Cursor for automated documentation, and a complete cycle of proposing, reviewing, implementing, and archiving changes.
The article explains that AI accelerates code generation but does not automatically shrink the whole delivery cycle, analyzes why traditional effort estimates become misleading, and proposes a six‑stage delivery‑focused model with practical principles and engineering safeguards for accurate estimation.
When AI eliminates execution bottlenecks, the real constraint becomes information flow, prompting a shift from hierarchical information‑routing to AI‑driven world models, intelligence layers and interfaces; the article analyses Block’s four‑layer architecture, its preconditions, challenges for mid‑level managers, and offers a step‑by‑step path for small teams to begin the AI‑native transformation.
Even after mastering Superpowers' three‑step flow, developers can still crash due to wrong execution mode, loose quality loops, or unfinished branches, so this guide provides a decision checklist, quality pipeline, Git worktree isolation, and a default recipe to reliably deliver medium‑to‑large changes.
The article outlines practical steps for project managers and team members to allocate time across multiple projects, emphasizing clear goals, weekly task breakdowns, resource matching, regular progress checks, effective communication, prioritization, and reusable modules to improve efficiency and reduce risk.
The article outlines a mixed project‑management approach for new initiatives, detailing three concrete steps—planning with milestone breakdowns, daily stand‑up supervision, and strategic resource allocation with a technical lead and a 15% time buffer—to ensure clear plans, transparent progress, and rapid issue resolution.
Finishing code does not mean the work is done; the Superpowers workflow adds mandatory review, isolated Git worktrees, and a structured branch‑finishing step to turn a code change into a safe, verifiable, and fully delivered engineering outcome.
This article examines how AI‑assisted coding tools combined with a domain‑driven, spec‑driven workflow (DDD‑SDD) can reshape backend development from ad‑hoc “vibe coding” to a disciplined, automated production pipeline, illustrated with a real‑world retail feature implementation.
The article details Xiaomi Robotics’ redesign of the CyberOne bionic hand—compressing its volume by 60%, boosting degrees of freedom by 64%, expanding full‑palm tactile coverage to 8200 mm², achieving over 150,000 grasp cycles, and introducing a bionic sweat‑gland cooling system to improve reliability for factory use.
This guide systematically outlines five key dimensions—quality strategy, team leadership, technical depth, metric‑driven improvement, and scenario‑based behavior—providing over 30 high‑frequency interview questions, model answers, code examples, and actionable frameworks to help candidates demonstrate sustainable quality leadership and secure a test manager role.
This guide explains how to overcome single‑project memory limits by combining Obsidian as a second brain with Claude Code's project‑level memory and a custom PM workspace, providing a structured, automated system for storing, syncing, and reusing knowledge across multiple development projects.
This article walks through the complete process of creating a Code Review Skill for AI agents, covering skeleton definition, architecture‑ and coding‑rule derivation, business‑logic checks, unit‑test standards, context routing, token‑consumption analysis, cost‑optimisation tips, and how to extend the pattern to other skills.
This guide explains why Claude Code often forgets previous context, demonstrates the shortcomings of a flat CLAUDE.md file, and provides a step‑by‑step method to reorganize project knowledge into a .claude directory with rules, memory, skills, and templates, enabling seamless cross‑project reuse and faster AI‑assisted development.
The article explains why the last mile of software delivery—merging, testing, changelog, version bump, documentation sync, and retrospection—is often the bottleneck, and introduces three automated skills (/ship, /document-release, /retro) that institutionalize these tasks to create a closed‑loop workflow.
The article explains that the most dangerous moment in development is when you think the code is finished, and introduces the /review and /investigate skills to catch structural risks and root‑cause issues before merging, especially in the AI‑driven era.
The guide reveals that while AI agents have reduced code generation costs to near zero, the true expense lies in ensuring quality, requiring engineers to shift from writing code to defining problems, designing agentic systems, and applying rigorous testing patterns such as red‑green TDD, context‑managed sub‑agents, and advanced Git workflows.
This article breaks down gstack's four pre‑implementation review skills—/plan-ceo-review, /plan-eng-review, /plan-design-review, and /design-consultation—explaining their distinct focus, when each should be used, common pitfalls, and the recommended sequencing to improve product planning before coding.
The article introduces the classic four‑quadrant (Eisenhower) time‑management framework, explains each quadrant with concrete research and daily‑life examples, and provides a three‑step practical guide to help graduate students prioritize tasks, reduce anxiety, and focus on what truly matters.
The article examines how a 2025 supply‑chain poisoning incident sparked a paradigm shift toward adversarial testing, introducing new roles, AaaS platforms, and dual‑cycle processes that move testing teams from merely finding bugs to building resilient, attack‑aware systems by 2026.
The article analyzes how AI reshapes product‑development workflows, forces a redefinition of the product manager role, pushes demand clarification ahead of engineering, upgrades governance, and shifts competitive advantage from manual delivery to structured knowledge assets, offering concrete recommendations for organizations entering the AI era.
