CASE STUDY · AI AUTOMATION & OPERATIONS

Reliable AI Media
Automation Pipeline

An observable workflow connecting RSS selection, deduplication, LLM editing, media generation, publishing, retry state, audit records and notifications. The emphasis is recoverability and review—not maximum output volume.

GitHub ↗

01 / Verified scope

Facts are derived from the current repository configuration and execution interfaces.

4content profiles: tech, consumer money, cybersecurity and science future
3publishing interfaces: YouTube, Instagram Reels and Facebook Reels
2notification channels: email and Telegram
3safe execution controls: dry-run, no-upload and privacy override

02 / Pipeline

Each stage leaves enough state to inspect what happened.

Reliable AI Media Automation Pipeline architecture
Source ingestion, event deduplication, LLM or local scoring, media generation, publishing adapters and operational records.

03 / Reliability

Deduplicationseen.json records processed entries and helps prevent the same event from being published again across feeds.
Failure Recoverypending_upload.json preserves a generated video after an upload failure so the next run retries publishing before generating another asset.
Audit TrailEach job stores the topic profile, source title, source URL, feed, result, failure stage and warnings.
Operational VisibilityJob records, per-platform publish status, daily logs and reports are separated, with email and Telegram notifications available.

04 / Architecture

RSS Sourcesround-robin candidates and full-text fallback
Editorial Guardprofile keywords, exclusions and dedup state
Generationselection, script, TTS, captions and assets
Operationspublisher adapters, recovery, records and alerts

05 / Attribution

Upstream framework and added engineering are separated explicitly.

The core video-generation engine is based on the portable MoneyPrinterTurbo project. The work demonstrated here is the surrounding automation and operations layer: four content profiles, RSS/editorial rules, dedup state, upload recovery, multi-platform interfaces, scheduling, job records, reports and notifications. The portfolio does not present upstream framework functionality as entirely built from scratch.

06 / Current limit

Quality-gate configuration exists, but it is not yet an enforced publish blocker.

The example configuration includes quality-gate fields, while the current main workflow does not call that check. The dependable review path today is to use --dry-run or --no-upload, inspect the selected topic or generated asset, and then choose whether to publish. Automated quality enforcement is therefore not claimed as complete.

07 / Takeaway

Reliable automation is more than connecting steps.

A maintainable pipeline needs source traceability, duplicate prevention, recovery state, audit records, notifications and a human-review path. Those operational properties are the main value of this project.