Reliable RSS Feeds
RSS is a lightweight publish/subscribe format that delivers clean, structured updates about what is new. In n8n, it allows you to ingest a feed, normalize each item, process entries with an LLM, and store the output in Airtable, offering a far more stable approach than scraping.
RSS is often a better choice than web scraping because it delivers structured updates, supports cheap and efficient polling with ETag or Last-Modified, and provides built-in deduplication through GUIDs. It is also far less fragile than scraping, which breaks whenever a site's markup changes.
In this demo, you will go end to end: find or build an RSS feed, trigger and read it in n8n, normalize incoming items, loop through them, use an LLM to generate tweets, and store the results in Airtable.
Look for an RSS icon or /feed link on blogs, Google News topics, or YouTube channels. If none exists, use a generator like RSS.app (native feeds are cleaner);
Create a free account, explore by topic, and copy the generated feed URL (XML works fine). Use filters to remove duplicates or limit by keywords;
Add an RSS Trigger (paste the feed URL) to test one item, then an RSS Feed (Read) node to fetch ~25 items;
In a Code or LLM node, clean the feed: strip HTML, extract the first image, and standardize fields like title, text, url, and publishedAt. De-dupe near-identical titles;
Loop through items one by one. The LLM writes a short, opinionated tweet per article, which is then saved to Airtable. Run a few items to verify output and tweak filters if needed.
You can now take any RSS feed, process its items, and automatically turn each one into a tweet saved in Airtable. Use simple aggregation for small feeds or normalize plus loop for larger ones. With a clean Code node, controlled batches, and reliable deduplication, you'll have a streamlined, reusable workflow for converting RSS updates into social content.
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Can you explain how the deduplication process works in this workflow?
How do I customize the tweet generation for a different topic or style?
What are the main steps to connect this workflow to my own Airtable base?
Awesome!
Completion rate improved to 4.17Reliable RSS Feeds
Sveip for å vise menyen
RSS is a lightweight publish/subscribe format that delivers clean, structured updates about what is new. In n8n, it allows you to ingest a feed, normalize each item, process entries with an LLM, and store the output in Airtable, offering a far more stable approach than scraping.
RSS is often a better choice than web scraping because it delivers structured updates, supports cheap and efficient polling with ETag or Last-Modified, and provides built-in deduplication through GUIDs. It is also far less fragile than scraping, which breaks whenever a site's markup changes.
In this demo, you will go end to end: find or build an RSS feed, trigger and read it in n8n, normalize incoming items, loop through them, use an LLM to generate tweets, and store the results in Airtable.
Look for an RSS icon or /feed link on blogs, Google News topics, or YouTube channels. If none exists, use a generator like RSS.app (native feeds are cleaner);
Create a free account, explore by topic, and copy the generated feed URL (XML works fine). Use filters to remove duplicates or limit by keywords;
Add an RSS Trigger (paste the feed URL) to test one item, then an RSS Feed (Read) node to fetch ~25 items;
In a Code or LLM node, clean the feed: strip HTML, extract the first image, and standardize fields like title, text, url, and publishedAt. De-dupe near-identical titles;
Loop through items one by one. The LLM writes a short, opinionated tweet per article, which is then saved to Airtable. Run a few items to verify output and tweak filters if needed.
You can now take any RSS feed, process its items, and automatically turn each one into a tweet saved in Airtable. Use simple aggregation for small feeds or normalize plus loop for larger ones. With a clean Code node, controlled batches, and reliable deduplication, you'll have a streamlined, reusable workflow for converting RSS updates into social content.
Takk for tilbakemeldingene dine!
