// Track

Your First AI Content Pipeline

Source → Draft → Image → Publish, on Autopilot

A build-something-today track. Assemble a four-stage content pipeline with a human-in-the-loop publish gate — the exact pattern that runs this very blog. Source topics, draft in your voice, generate hero images with a fallback chain, and ship via Git PR.

8 lessons~69 min total

Lessons are shown in recommended order. Complete them in sequence for the best experience — or jump to any lesson.

Lesson 360·8 min read

Why Automate Content? The Compounding Case

Every article you publish manually is a one-time event; every article your pipeline publishes while you sleep is a deposit into a compounding asset.

Lesson 361·7 min read

The Four-Stage Pipeline at a Glance

SOURCE feeds DRAFT, DRAFT feeds IMAGE, IMAGE feeds PUBLISH — four clean handoffs that turn a topic queue into a live article.

Lesson 362·9 min read

Sourcing: Where Good Topics Come From

The quality of every article your pipeline generates is bounded by the quality of what you fed the source stage — garbage in, garbage out, at scale.

Lesson 363·10 min read

Drafting in Your Voice (Voice Profiles)

Generic AI prose is detectable in seconds — a voice profile is the spec that tells the model to write like you, not like everyone else who uses the same model.

Lesson 364·8 min read

Hero Images and the Fallback Chain

A single image API failure should not kill your pipeline — a fallback chain keeps generation running when your primary provider goes down or hits rate limits.

Lesson 365·8 min read

Publishing via Git PR: Human in the Loop

Auto-generating is not the same as auto-publishing — a pull request is the review gate that keeps quality high and brand damage low while the pipeline calibrates.

Lesson 366·9 min read

Putting It on a Schedule (Cron)

A pipeline that only runs when you manually trigger it is a chore — a pipeline on a cron is infrastructure that compounds while you focus on other things.

Lesson 367·10 min read

Measuring What Actually Works

A pipeline that publishes without measuring is flying blind — closing the feedback loop is what transforms a content machine into a content strategy engine.