News automation | AINA Blog

AI news generation in production is a pipeline, not a single chat prompt. Signals enter as RSS or API items, are normalized and deduplicated, optionally clustered, then turned into drafts under strict templates. Translation, images, and publishing each have their own failure modes—so each stage needs observability. Each stage should emit structured logs: item ID, model version, template ID, latency, and error class. Without that, “the AI wrote…

How to Create a News Site Without an Editorial Desk

You do not need a classic newsroom on day one. What you need is a clear vertical, a trusted set of RSS inputs, and a publishing system (WordPress works well) that can accept structured posts on a schedule. The goal is to ship readable stories with traceable sources—not to mimic a twenty-person desk. Think of the first months as a product launch, not an editorial audition. Your “MVP” is a pipeline that never invents facts, always attributes, and…

News Generation from RSS: End-to-End

End-to-end means every hop is owned: fetch schedules, normalization, dedupe keys, novelty scoring, drafting, media upload, WordPress categories, publish, and archival for audit. If you cannot draw a box diagram from fetch to live URL—with owners and SLOs per box—you are not “doing RSS news” yet; you are experimenting. Production systems need clear handoffs and rollback points. Reference pipeline If any step fails, you need idempotent retries and…

RSS to Article: Content Automation

RSS is the most boring input—and that is a compliment. Feeds are predictable, cacheable, and easy to monitor. That makes them ideal for factories that must run 24/7 without babysitting a proprietary API. Unlike one-off scrapers or opaque vendor APIs, RSS gives you stable URLs, timestamps, and item identifiers you can key on. Your stack can treat each item as a versioned fact: fetch, hash, decide whether it is novel enough to become an article…