Engineering Guides

Choose the right browser-first AI builder for your first project without overcommitting to a developer workflow too early.

Build a first website with an AI builder, then harden it enough for a real custom-domain launch.

Pick your first AI development path by workflow shape, not by hype cycle or model branding.

Use clearer UI language, state descriptions, and constraints so AI tools build the interface you intended.

Prompt packs for using a general AI assistant to choose the right engineering workflow, tools, and guardrails for your situation.

Set up VS Code as a durable AI coding environment without giving up control of your normal editor workflow.

Use Cursor as an AI-first IDE without turning agent output into an unreviewed side channel.

Use chat tools for planning and review, then hand work cleanly into your editor or terminal agent.

Match coding tasks to model classes so you spend your strongest models where they matter and keep faster paths cheap.

Keep AI coding work inside calm, reviewable Git and PR habits instead of turning it into a hidden side channel.

Set the instruction files, approval rules, and review gates that keep AI coding agents useful instead of expensive chaos.

Understand what MCP changes in AI engineering workflows and where governed tool access is actually worth the complexity.

Run AI coding agents from the terminal with explicit planning, approvals, and test loops instead of treating the shell like a magic box.

Use background or delegated coding agents without turning code review into a ceremonial afterthought.

Choose the right hosting lane for an AI-built project by architecture shape: static, SSR, or full-stack service.

Build a practical local or hybrid coding workflow with current open-weight models and explicit privacy tradeoffs.