AI Transparency

I wear the „100% Vibe Coded“ stamp on the home page with a wink — but honestly, it undersells things. What happens here has a more precise name: Spec-Driven Development. Not „I type wishes into a chat and see what comes out“, but: I write the specification — directives, skills, coding and architecture guidelines, concepts for every article and every demo. That's the source of truth. The implementation is handled by a team of AI agents:

• Dispatchers (virtual project managers) break my intent down into tickets and fill a task queue.
• Developer, test, architecture, UX and review agents work through the tickets — often in tandem: one builds, one checks. Checks and balances, like in a real team.
• The whole thing runs in loops: one agent keeps the others moving, monitors progress, starts new work. There's actually a term for it as of 2026 — Loop Engineering: „loops that run while I sleep.“

My role? I hardly write code myself anymore. I set the architecture, design system, concepts and decisions, and I put up the quality gates. The classic junior tasks — typing, translating, testing through, repeating — take care of themselves.

The best part: I don't even have to sit at the computer. Earlier, while I was writing the book, today while gardening or simply watching YouTube — I dictate my requirements. Thanks to speech recognition (Whisper) they land in an inbox, my dispatchers turn them into tickets, and the next morning the features are there.

So no false impression arises: I did not write the vast majority of these tickets myself — that's handled by my virtual project managers. An example — I say just one sentence:

„From now on we support Swahili.“

What then unfolds, without me intervening any further:

1. 1 First the system creates its own working branch and checks it out — a „feature branch“, here feature/add-swahili. Everything that follows happens there in isolation, so the live site stays stable at all times.
2. 2 A dispatcher reads the request and notes: translation guidelines for Swahili are missing.
3. 3 It commissions thorough research into idioms, expressions and language peculiarities and gathers it all into a translation guideline.
4. 4 It checks: are there rules for Plain Language in Swahili? No → so it derives them from German and English.
5. 5 It sets up review tickets: do structure and quality match the other language guidelines? A test text is translated, a reviewer checks it.
6. 6 Once the guidelines are solid, the system adds Swahili to the central language list, extends the language switcher in the menu (flag, alphabetical position) and wires up every place in the code that needs to know about the new language — from the SEO locale to the ELIZA chatbot.
7. 7 Now a script runs that maps out the entire portal — every single page — and creates a translation task into Swahili for each route.
8. 8 As soon as a worker has translated a page, the system automatically spawns the next ticket: a quality review for exactly that page. automatic
9. 9 Once all routes are translated and checked, the system builds the language bundles — content, interface texts and the glossary highlighting — so that Swahili actually appears in the browser. automatic
10. 10 It commits the work and runs a full production build as an integrity check — if that fails, the chain stops and reports back to me.
11. 11 Once the build is green, the system merges the feature branch back into the main line — and Swahili goes live.

From a single sentence, in one night, hundreds of tickets emerge — commissioned and worked through by a team I never personally brief.

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