April 10, 2026 · 4 min read
THE AI NEWSROOM — ONE DEVELOPER, THREE AGENTS, ONE BOTTLENECK
I built an AI-assisted publishing system. The interesting constraint was not writing articles — it was deciding what is safe and good enough to publish under my own name.
The system can produce faster than I can verify. That sentence is the entire story of this project.
I built The AI Signal, a blog about AI-assisted development that is itself AI-assisted from top to bottom: research, writing, illustration, even editorial strategy. The pipeline can draft, illustrate, and stage an article without me touching it. But nothing ships until I read it, check the sources, and tap Approve on my phone. That is the bottleneck. And it turned out to be the most interesting design constraint of the entire project.
This is the first in a series of posts about building this system. Not the polished marketing version — the real one, with the bugs, the surprising costs, and the moments where the architecture fought back.
WHY BUILD THIS
I have been running a self-hosted VPS for a while now, deploying side projects with Nginx, Let’s Encrypt, and whatever stack fits the problem. When vibe coding started gaining traction — Claude Code, Cursor, Windsurf, the whole wave — I noticed something: most coverage was either hype or dismissal. Very little sat in the middle, where practitioners actually work.
So I thought: what if an AI wrote about AI development, transparently, with a clear editorial voice — and a human who takes legal responsibility for every word published?
Not a content farm. Not GPT slop. A genuine attempt at automated journalism with human oversight baked in from day one.
THE HUMAN IN THE LOOP
Here is the part that matters most, and the part most “AI automation” stories gloss over: I review and approve every single article before it goes live.
This is not a nice-to-have. It is a requirement. Legally, I am the publisher. If an article contains misinformation, a hallucinated source, or a take that does not hold up — that is on me. No amount of automation changes that responsibility.
The system is designed around this constraint. The pipeline does not publish autonomously. It builds a preview, sends it to Telegram, and waits. I read the article, check the sources, evaluate the angle. Then I tap a button — or I do not.
Some weeks that means I approve three articles in a day. Other weeks, nothing goes out. An early publishing gap made one thing clear: the bottleneck is never generation — it is always review. The system can produce faster than I can verify.
That tension is not a bug. It is the architecture working as intended.
THE ARCHITECTURE IN 30 SECONDS
The AI Signal runs as a monorepo on a single VPS. Five moving parts:
The Site — Astro-based, statically built, served by Nginx. Vanilla CSS, no framework overhead. Each article category gets its own visual treatment: opinion pieces have kinetic typography animations, breaking news gets cascading code rain, technical articles use AI-generated imagery.
The Pipeline — A TypeScript pipeline that researches trending topics, drafts articles, generates images via a local ComfyUI instance connected to Black Forest Labs’ Flux model, and sends the finished draft to my phone for review.
The Admin Dashboard — An Express app behind Basic Auth where I can trigger generation, preview articles exactly as they will appear on the live site, approve or reject drafts, and monitor system status.
The Telegram Bot — When a draft is ready, I get a notification with a preview link and two buttons: Approve or Reject. One tap from my phone publishes the article and triggers a rebuild. That is the entire approval workflow.
The Night Council — This is the one that raises eyebrows. Three AI agents — a Creative, a Strategist, and a Critic — debate the site’s direction in scheduled sessions. Their recommendations are saved as structured briefs that inform future content decisions. More on this in a later post.
WHAT YOU WILL NOT FIND HERE
This is not a tutorial on how to prompt ChatGPT. The AI Signal runs on Claude, uses Model Context Protocol for web research, generates images through a local Flux pipeline, manages state through markdown frontmatter, and deploys via shell scripts and PM2. It is opinionated, specific, and built for one person to operate.
But the patterns are transferable. How do you build a content pipeline that is reliable enough to trust but transparent enough to audit? How do you give AI agents real editorial responsibilities without losing control? How do you keep costs under a euro per day?
Those are the questions I will be answering in this series.
WHAT IS COMING
Over the next few weeks, I will walk through each layer of the system:
¬ The Content Pipeline — from a blank prompt to a published article, including every step where things can go wrong
¬ The Night Council — how three AI personas debate strategy and what they have actually recommended
¬ The Visual Identity — kinetic typography, AI-generated art, and the design system that ties it together
¬ The Real Costs — token usage, API bills, hosting, and what I have learned about building sustainably
The AI Signal is live at vibe.cerridan.com. Every article on it was written by Claude and approved by me. The footer says so. That transparency is not a limitation — it is the entire point.
This is Part 1 of a 5-part series on building The AI Signal. Next up: the content pipeline, from research to publication.
Christian Scherling is a designer and developer at cerridan | design e.U., building AI-assisted tools and wondering how far automation can go before it stops being useful.