Building an Automated Content Pipeline That Posts to 6 Platforms

Every developer knows the pain: you write a great article, publish it on one platform, and then spend the next hour manually reformatting and posting it everywhere else. LinkedIn wants a professional tone. Twitter needs a 280-character hook. Dev.to wants proper frontmatter. Instagram needs an image.

I got tired of this, so I built an automated content distribution pipeline that publishes to 6 platforms from a single source of truth. Here's how.

The Architecture

The system is built on a simple principle: write once, distribute everywhere.

Hashnode (source of truth)
  └──► Cross-Post Service
       ├──► Dev.to (API, canonical URL back to Hashnode)
       ├──► LinkedIn (Marketing API v2, article share)
       ├──► Threads (Meta API, text teaser)
       ├──► Instagram (Content Publishing API, card image)
       ├──► Twitter/X (API v2, 280-char hook)
       └──► daily.dev (auto via RSS + Squad)

Each platform gets a tailored variant — not a blind copy-paste. The pipeline understands platform-specific constraints:

The Platform Adapter Pattern

Each platform implements a common interface:

class PlatformAdapter(ABC):
    RATE_LIMIT: tuple[int, int]  # (max_requests, window_seconds)

    @abstractmethod
    async def publish(
        self, title: str, body: str,
        canonical_url: str | None = None,
        tags: list[str] | None = None,
        metadata: dict | None = None,
    ) -> CrossPostResult: ...

    async def check_rate_limit(self) -> bool:
        # Redis-backed sliding window
        ...

This means adding a new platform is just implementing one class. The cross-post orchestrator doesn't care about platform-specific details — it just calls adapter.publish().

Smart Content Composition

The hardest part isn't the API calls — it's making content feel native to each platform. Nobody wants to read a 2,000-word blog post crammed into a tweet.

For Twitter, the composer prioritizes:

1. Body text (or title as fallback)
2. Canonical URL (t.co wraps to 23 chars)
3. Hashtags (only if space permits)
4. Truncate with ellipsis at 280 chars

For Threads, it's different — title and body are joined, links are validated (max 5 unique URLs), and text is capped at 500 chars.

The Quality Ladder

Before any content goes out, it runs through a quality ladder:

1. Ollama (local, free) generates the first draft
2. A deterministic quality gate checks readability, hashtag density, and boring-start patterns
3. If it fails, Claude Haiku polishes it (~$0.002 per variant)
4. If still fails, template fallback (guaranteed output)

This keeps costs near zero for 80% of content while ensuring quality.

The Feedback Loop

A weekly Celery task evaluates published content performance:

• Pulls engagement metrics (likes, comments, shares) per platform
• Compares top performers vs bottom performers
• Extracts patterns into a LearningEmbedding table
• These patterns are injected into future content generation prompts

Over time, the system learns what works on each platform and adapts.

Secrets Management

With 6+ platforms, credential management gets messy fast. Every secret lives in 1Password and is referenced via op:// URIs in a single .env.tpl file. Both dev and prod resolve secrets at runtime — no .env files committed, ever.

What's Next

• Performance dashboard — visualize engagement metrics per platform in the UI
• Token refresh automation — OAuth tokens expire (LinkedIn: 60 days, Meta: 60 days)
• RSS feed — already serving published content for aggregators
• WhatsApp broadcast — notify subscribers when new content drops

The Stack

• Backend: Python 3.13, FastAPI, SQLAlchemy 2 (async), Celery, PostgreSQL + pgvector
• Frontend: Next.js 16, TypeScript, TanStack Query, Tailwind, shadcn/ui
• AI: Quality ladder (Ollama to Haiku), RAG enrichment, performance learning loop
• Infra: 1Password secrets, Redis rate limiting, Hetzner VPS, Cloudflare tunnel

The entire pipeline — from content creation to multi-platform distribution — runs from a single dashboard. Write once, publish everywhere, learn from the results.

This post was itself cross-posted to multiple platforms using the pipeline described above.