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The editorial pipeline

Every article we publish walks through the same pipeline — from raw data to published story to social distribution. Let's pull back the curtain.

Discovery: finding the signal

The pipeline starts before any AI sees the story. We cast a wide net across multiple sources:

  • Spaceflight News API (SNAPI) — aggregated aerospace news
  • Launch Library 2 — launch schedules, mission data, verified facts
  • X/Twitter (via Rettiwt-API) — open-source Node.js scraper we run VPS-side; pulls the latest posts from official accounts (Rocket Lab, Blue Origin, CSA, NASA, and others)
  • RSS feeds — SpaceQ and other aerospace outlets
  • Crawl4AI — open-source LLM-ready web scraper (Playwright-backed), wrapped in our article_scraper.py and invoked over SSH on the VPS. Handles JS-heavy sites and most anti-bot blocks. For sites where Crawl4AI gets flagged, we route by tier: CRW self-hosted (SpaceX + Starlink update pages, plus Space Daily article bodies — a Rust scraper with automatic stealth-JS injection running alongside n8n on our OVH VPS), a plain n8n HTTP Request with browser-like headers (CSA newsroom, Rocket Lab news), or ScraperAPI as a paid fallback for pages behind Vercel Security Checkpoint (Blue Origin).
  • Wikipedia lookups — historical context and background details

These sources feed into Robo Chris (our curator system) continuously. No filtering yet — just collection.

Why multiple sources?

One source alone misses stories. NASA announcements hit LL2 first. SpaceX milestones break on X before traditional outlets pick them up. Combining them gives us breadth and speed.

Curation: Robo Chris decides what matters

This is where Robo Chris earns its name. The curator system:

  1. Deduplicates — same story across five sources? Pick the best version.
  2. Filters — is this actual news, or promotional fluff?
  3. Weights — which stories rise to the top? Canadian angle? Commercial space? Scientific breakthrough? Weights adjust per workflow.
  4. Gates by schedule — daily stories always go; weekly reports check the day of the week; monthly reports check the week of the month.

The curator produces a ranked candidate list. Nothing gets dropped — we just order them by fit.

The human still matters at every step

Robo Chris doesn't publish anything. It recommends. If something smells wrong (or a big story got missed), Chris can manually override the ranking or add stories before drafting begins.

Drafting: the LLM author

Once curation is done, the top stories go to the LLM author. We use:

  • Primary: Qwen 3.7 Plus (via OpenRouter) — clean, structured HTML output, cost-effective
  • Fallback: Claude Haiku 4.5 (via OpenRouter) — kicks in when Qwen stalls or errors mid-response
  • Fact-check / editor: OpenAI GPT-5-mini — runs after every draft, verifies claims against sources, tightens SEO
  • Social captions: xAI Grok — handles the Facebook/Instagram excerpt writing

The author doesn't freestyle. It works from author rules — a structured prompt that includes:

  • Tone and voice (second-person, slightly nerdy, transparent)
  • Article structure (opener, body, source links, byline)
  • Length targets — a daily broadcast is roughly 600–900 words across 2–3 stories; weeklies + monthlies run longer
  • House style (hyperlinks, source attribution, no AI hype)
  • SEO guidance (keyword placement, meta description)

The output is a first draft with title, body, featured image candidate, and social excerpt.

Editing & fact-checking

Before anything publishes, an LLM fact-checker runs:

  1. Identifies claims — pulls out factual assertions (dates, numbers, agency names, mission details)
  2. Cross-references sources — checks claims against the original articles we cited
  3. Flags discrepancies — if something doesn't match, it raises a flag for human review
  4. Signs off — if everything checks, it marks the article ready for Chris

This is not a catch-all. The fact-checker works from the sources we already have. But it prevents copy-paste errors and catches numbers that got mangled in summarization.

Fact-checking in the pipeline

Every article gets a fact-check pass. If the checker flags something, Chris reviews the sources manually. No article publishes with open flags.

Human review: the non-negotiable gate

Every article — daily broadcast, weekly report, monthly report — is read by Chris before it publishes. He reads the draft, opens the sources, cross-checks the facts, and either approves, edits, or sends back for revision.

The AI drafts. The human decides. Nothing goes live without a real person putting their name on it.

Publishing: WordPress API

Approved articles go to the blog via the WordPress REST API:

  • Title, body, featured image
  • SEO metadata (generated earlier)
  • Author byline (with the drafting model credited)
  • Categories and tags
  • Publish timestamp

Images pull from our tcs-images GitHub repository — a folder structure organized by date and topic.

Distribution: social + RSS

The moment an article publishes to WordPress:

  1. Facebook & Instagram — automated posts fire with the excerpt, link, and featured image
  2. RSS feed — updated immediately; subscribers see it in their readers
  3. Syndication — the full article is live, indexable, and discoverable

This happens in parallel. No manual posting needed.

The flow: a real-world example

Follow a single story — say, a Starship test flight breaking on SpaceFlightNews — as it travels from raw feed to your Facebook timeline:

graph TD
    A["📰 <b>SpaceFlightNews API</b><br/><i>New story: 'Starship test flight'</i>"]
    B["🤖 <b>Robo Chris curator</b><br/>dedup + filter + rank across sources<br/><i>→ top ~8 stories for today's broadcast</i>"]
    C["✍️ <b>LLM Author (Qwen)</b><br/>drafts each story per author rules<br/><i>title, body, image, social excerpt</i>"]
    D["🔍 <b>Fact-check LLM</b><br/>verify claims against sources<br/>+ generate SEO tags"]
    E["👤 <b>Chris</b><br/>read, edit, approve — the human gate"]
    F["📡 <b>WordPress</b><br/>publish approved article"]
    G["📱 <b>Facebook + Instagram</b><br/><i>auto-post excerpt + link</i>"]
    H["📶 <b>RSS feed</b><br/><i>subscribers see it immediately</i>"]

    A --> B --> C --> D --> E --> F
    F --> G
    F --> H

    classDef data fill:#0A1428,color:#fff,stroke:#FF9D3D,stroke-width:1px
    classDef ai fill:#FF9D3D,color:#000,stroke:#FF9D3D,stroke-width:1px
    classDef human fill:#22C55E,color:#fff,stroke:#22C55E,stroke-width:2px
    class A,F,G,H data
    class B,C,D ai
    class E human

Legend: dark blue = data source / distribution · orange = AI stage · green = human gate

From SNAPI ingestion to RSS subscribers seeing the story: under 60 minutes for daily broadcasts. Weekly and monthly pieces take longer because they cover more ground and pull from more sources — but the pipeline is the same.

Next: Meet Robo Chris →