From idea to production

Know the problem, who has it, and what done looks like before a single line of code.

Check what exists before you build it. The best code is code you didn't have to write.

Set the shape of the system before asking an agent to generate it. Start simple and grow on evidence.

A deliberate visual system, so the product looks designed, not generated.

Make the next action obvious and every action answer back.

Build it in from the first component. A final audit finds problems too late and too expensive to fix.

Readable, typed, boring code. Clarity is what lets people and agents change it safely later.

Ship less JavaScript, render on the server where it helps, and keep the main thread free.

Agents can read, edit and run your repository. You stay responsible for every line they write.

The definition of done for every agent task. Far more useful than telling an AI not to look vibe-coded.

A predictable contract that's documented, validated and safe to retry.

Optimize the query before you scale the database, and cache with a clear invalidation plan.

Pick a gateway or a merchant of record, then let verified webhooks drive every order.

Plan for other languages early. Retrofitting i18n touches every string in the app.

Anchor on OWASP Top 10:2025 for awareness and OWASP ASVS when you need a verifiable standard.

Treat coding agents and MCP servers as privileged automation, not magical employees.

Every dependency, action and credential is part of your attack surface.

Collect less, keep it for less time, and let people take their data with them.

Test where failure hurts most first, then check it in real browsers and devices.

Automate the checks so nothing, and no agent, can skip them.

Fast and measurable. Optimize Core Web Vitals with data from real users.

Make every page crawlable, understandable and worth ranking.

Generative engine optimization: make your content easy for AI systems to understand, retrieve and cite.

One-command setup, automated pipelines, and environments that match production.

Know how to roll back before you roll out.

See what's slow, failing, expensive and unused, from production evidence.

Watch spend like a metric, and scale only the part that's actually the bottleneck.

For AI products: evaluate outputs like you test code, so a model update can't silently break production.

Decide what happens when everything breaks, before it does.

A project people can install, understand and contribute to in minutes.

A project isn't finished when it's deployed. Keep a weekly and monthly rhythm.