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ai guide

Lessons from the Trenches: What AI Native Teams Must Get Right

Not everyone should use a coding agent to modify code directly. AI Native teams need interface specs, test-first development, monorepo, security guardrails, human-in-the-loop, and token budget controls. Building an agent platform layer on top of coding agents and clearly redefining developer roles is the right path forward.

tech guide

GitHub Actions: A CI/CD Primer and Monorepo Strategy

GitHub Actions is the lowest-friction CI/CD tool available today, ideal for small-to-medium projects. The key to monorepos is using path filters so only affected apps trigger a build.

A One-Person Full-Stack Team: AI-Driven Development Workflow from OpenSpec to Auto-Deploy

Use OpenSpec to break requirements into engineering tasks, Claude Code to implement them, hooks to auto-format and protect, local review before committing, three AI reviewers running in parallel on PR, and auto-deploy after merge. This entire workflow lets one person maintain quality across six sub-projects.

Claude Code's Three-Layer Quality Defense: Hooks, Skills, and Instruction Files

Hooks are automated safety nets (blocking bad commits), Skills are interactive workflows (running checks + auto-fixing), and instruction files (CLAUDE.md / AGENTS.md) are behavioral guidelines. Each layer operates independently, but together they enable an AI agent to automatically run lint, typecheck, and build checks before every commit.