A hands-on series for turning AI coding from personal productivity into team engineering practice, covering Spec Coding, Vibe Coding, OpenSpec, Prompt CI/CD, rules files, context engineering, code review, test generation, and maintainable AI IDE workflows.
A provider-neutral guide to customizing AI coding assistants without treating instruction files as security controls. Define project context, coding conventions, task boundaries, verification steps, permissions, and review gates; test configuration changes and keep secrets, identity, and side effects outside model-controlled instructions.
Learn Spec Coding as a requirements-to-evidence discipline: define scope, constraints, acceptance criteria, traceability, change control, and conformance gates.
Write implementation-ready AI coding specs with explicit intent, acceptance scenarios, constraints, design decisions, and reviewable tasks. Uses one OpenSpec artifact set as a worked example while keeping command installation and lifecycle operations in the dedicated OpenSpec tutorial.
Learn OpenSpec step by step with a Spec-Driven Development workflow. Use /opsx:propose, /opsx:apply, and /opsx:archive to plan and implement AI coding changes.
Learn what Vibe Coding means, how its AI-first workflow works, which tools to use, and where production risks begin. Includes guidance for safer delivery.
A hands-on Vibe Coding workflow, from choosing an agent-capable tool to shipping. Compares Cursor, Claude Code, and Trae by shape rather than ranking, then walks through .cursorrules as a convention aid, intent prompting, a 10-minute finance-dashboard demo, small-step iteration, and the guardrails — tests you actually read, spec grounding, and non-negotiable security boundaries — that keep generated code safe.
Choose a Vibe Coding tool by workflow rather than hype. Compare Cursor, Windsurf, TRAE, and Claude Code by best-fit task, context model, rule files, agent mode, data boundary, and cost, then run a controlled, reversible trial on your own repository.
Cursor 3 reframes the IDE around agents instead of files. This guide explains the durable design ideas behind it—the agent workspace, cloud agents on isolated VMs, a purpose-built coding model, self-improving review, and canvases—and how to evaluate whether each idea helps your team, treating version-specific models, prices, and benchmarks as dated claims to verify at the source rather than facts.
Compare AI coding tools by workflow, execution boundary, data handling, security, governance, portability, and total cost before testing a team shortlist.
A practical framework for comparing AI coding rule files across host tools. Pin the exact tool version and verify discovery, precedence, scope, and execution behavior; distinguish untrusted context from trusted policy; and choose a maintainable context contract for team workflows.
Tired of AI-generated code smelling like garbage? Learn how to guide LLMs to output high-quality, maintainable code using Engineering Standards, Spec-Driven Development (SDD), and advanced Prompt Engineering. Featuring Trae/Cursor rules and real-world examples.
A workload-first method for evaluating AI coding assistants and team adoption. Separate vendor claims from causal evidence, build a baseline, measure delivery and quality together, account for review and rework, calculate cost per successful outcome, and set privacy, security, learning, and rollback gates without assuming a universal productivity gain.
Explore the fundamental transformation of developer roles in the AI era. From writing code line-by-line to orchestrating AI agents, learn the new skills, workflows, and mindset shifts needed to thrive as an 'Agent Shepherd' in 2026 and beyond.
A hands-on workflow for building a full-stack project with Claude Code end to end: CLAUDE.md setup, Plan Mode, vertical slices, testing, review, and deployment prep — plus the control and security boundaries to keep firm when an agent has real access to your files, shell, and repository.
A practical guide to Cursor 3 Background Agents: how async delegation actually works, five workflow patterns that hold up in daily use, how to configure rules and cloud environments, the boundaries where agents struggle, and the security limits to keep firm when agents run unattended and connect to your systems.
Build an AI code review pipeline that treats model findings as evidence, not verdicts. Learn secure workflow isolation, line validation, evaluation gates, human review, and test-gap verification.
How we used OpenSpec, Spec Coding, and AI agents to build a full SBTI personality test site in half a day — proposals, specs, tasks, scoring, radar charts, and poster generation.