How I Use AI as a Full-Stack Developer: My Real Automation Stack

People ask me some version of the same question: "you build websites and AI solutions — so how much of your own work is AI doing?" The honest answer is layered. AI writes a lot of the first draft of my code. It does none of the judgment calls. It runs whole pipelines in this project unattended. It never touches production without a human reading the diff first. Below is the actual stack, not the pitch version.

1. Code: pair programming, not autopilot

Most of my day-to-day AI use is Claude Code sitting next to me in the terminal, inside this exact repository. I describe a component, a bug, a refactor across the seven language folders under src/app, and it drafts the change. I read every diff before it lands — the same way I would review a junior's PR. AI is very good at "apply this pattern to 40 files consistently" and noticeably worse at "decide whether this pattern should exist." So I keep the second job.

The concrete wins: scaffolding a new page factory once and letting AI wire up every locale variant from it, writing the first pass of a Vitest suite for a new lib function, translating a stack trace into a plain-English root cause before I go digging myself, and drafting commit messages from a diff so I do not stare at a blank cursor.

2. Content and i18n pipelines that run without me

This site ships every blog post in up to seven languages: en, ua, de, fr, es, it, tr. I write the English and Ukrainian versions by hand — that part stays mine, because tone and technical precision matter and I do not want a machine-translated voice as my own. The remaining languages are filled in by scripts in scripts/ that walk the localized content tree and call a translation API for any field missing a locale, cache the result so re-runs do not re-pay for strings I already have, and fail loudly instead of silently writing an empty string into a page.

The same automation instinct shows up outside the blog: sitemap.ts regenerates every route across every locale at build time instead of me maintaining a list by hand, the RSS feed and llms.txt / llms-full.txt files are derived from the same post data so they cannot drift out of sync with what is actually published, and CI runs the full test and lint pass on every push before anything reaches the nextjs.yml deploy workflow.

3. AI inside the product, not just around it

The other half of "AI in my workflow" is what I ship for GEO — generative engine optimization, making a site legible to an LLM the same way SEO makes it legible to a search crawler. Concretely: llms.txt and llms-full.txt describing this site in a format an agent can parse directly, JSON-LD structured data on service and blog pages so a model can extract facts instead of guessing from prose, and a schema markup component that stays in sync with the actual page content instead of a static block someone forgot to update.

That is the boundary I actually care about: AI helping me build the site is invisible to a visitor. AI helping the site itself be understood by other AI is a feature I am explicitly building and testing — there is a dedicated Vitest suite for the GEO feed helpers, exactly so this does not quietly rot the way an untested integration eventually does.

4. Where I keep the human seat

None of this means AI decides what ships. It does not choose the information architecture, it does not pick which service pages exist, it does not decide that a contact form needs one more validation rule instead of three fewer fields — that judgment is the actual job, and it is exactly the part clients are paying for when they hire a developer instead of a generator. AI drafts; I merge. AI translates; I write the source. AI proposes a refactor; I decide if the abstraction earns its place, following the same discipline of not adding structure the codebase does not need yet.

If you are trying to figure out where AI actually saves time in a real product — versus where it just moves the review burden around — that is usually the right question to ask before automating anything: what decision am I actually removing from a human, and am I okay with that decision being made by a script?

Want this set up for your project?

I build this kind of AI-assisted, automation-first workflow into client projects too — from CI-gated deploys to GEO-ready content structure. Reach out via the contact form and tell me what part of your process still eats your week.