How to Build a Reliable Telegram Post Monitor Using Apify and n8n

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A developer tutorial explains why a scheduled scraper alone cannot detect new Telegram posts without a separate state-tracking layer. The core problem is that 'new' requires comparing the current data snapshot against a previous one — information a single Actor run does not hold on its own. The author built a solution using a stateless Apify Actor for scraping and an n8n workflow to manage comparison state, duplicate prevention, and first-run policy. Testing across four scenarios revealed that a fresh session with no baseline can retrieve posts but cannot determine which are new — a distinction the system must explicitly preserve. The design is presented as reusable for price trackers, job monitors, inventory checks, and any workflow that repeatedly asks what has changed.

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