AI Workflow 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 workflow lane by operating model, not by hype cycle or tool branding.

Prompt packs for choosing the right AI workflow lane, tool stack, and guardrails for your current work.

Turn fuzzy ideas into useful task briefs by naming the goal, evidence, constraints, approval boundary, and output shape before you ask the model to do the work.

Give the model the right evidence, constraints, approval boundaries, and examples so it stops guessing what matters and starts producing work you can actually use.

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

Use workspace-native agents and connected tools for recurring team workflows without confusing AI assistance with uncontrolled automation.

Plan in one surface, execute in the right one, and review in a third when the task needs stronger separation.

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.

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.

Diagnose weak outputs, rewrite prompts deliberately, and decide when the real problem is missing context, weak constraints, or the wrong workflow.

Build a small AI-assisted game prototype by locking the loop first, using AI for scoped implementation and draft assets, then playtesting before you expand.

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 workflow lane and architecture shape: builder-first, repo-first static, SSR, or fuller service.

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