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.
Learn how to write an implementation-ready AI coding spec with observable requirements, API and state contracts, failure cases, traceable tasks, Go tests, and release evidence.
Use OpenSpec in a real codebase: install the CLI, choose the right AI workflow command, review proposal and delta specs, implement a Go change, verify evidence, sync safely, and archive without confusing documentation with correctness.
Vibe Coding means describing software intent in natural language and judging generated behavior through rapid execution loops. Learn its origin, workflow, evidence, suitable use cases, production limits, and the controls needed before AI-generated code can safely ship.
Follow a reproducible Vibe Coding workflow on a real Go feature: establish a clean baseline, write an acceptance contract, provide minimal repository context, constrain an AI coding agent, inspect the diff, run deterministic tests, challenge edge cases, and preserve evidence for review and rollback.
A provider-neutral operating model for secure Vibe Coding. Convert requests into bounded task contracts, classify context by trust, constrain tools and side effects, verify generated Go code, preserve provenance and rollback, and measure accepted outcomes instead of trusting fluent drafts or raw code volume.
Compare Cursor, Windsurf, TRAE, and Claude Code with a reproducible evaluation instead of a static ranking. Map each tool's interaction model, rule system, execution boundary, security controls, and best-fit workload, then score the same repository tasks by acceptance, review effort, scope compliance, recovery, and total cost.
Cursor 3 turns the IDE into a control plane for local and cloud coding agents. This evidence-based guide explains its agent workspace, isolated cloud environments, local-to-cloud handoff, context and review surfaces, security and data boundaries, and a two-week evaluation with a runnable Go release gate so teams can judge merge-ready value rather than demos or benchmark claims.
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.
Improve AI-generated code quality with explicit change contracts, repository-aware context, layered deterministic checks, structural oracles, security review, and production evidence. Learn why passing tests is necessary but insufficient, how to control dependencies and side effects, and how to measure maintainability without trusting model confidence.
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.
An evidence-led guide to the developer role in the AI era. Learn what research says about planning versus execution, why productivity claims remain task-dependent, which responsibilities stay human-owned, and how to build specification, review, domain, security, and evaluation skills without relying on replacement hype.
Build a real Go project with Claude Code using an acceptance contract, CLAUDE.md, Plan Mode, vertical slices, executable tests, review gates, sandboxing, hooks, checkpoints, and a release evidence packet.
Use Cursor Cloud Agents with reproducible environments, bounded repository access, secret and egress controls, evidence-based task contracts, draft pull requests, review gates, retention decisions, and rollback-ready handoff.
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.
Use an SBTI quiz as a concrete OpenSpec and Spec Coding case study. Define observable requirements, model questions and result templates as versioned data, implement deterministic scoring, test routing and poster generation, verify bilingual parity, and keep AI-generated code behind reviewable acceptance gates instead of relying on speed claims.