{"id":804,"date":"2026-09-11T16:01:17","date_gmt":"2026-09-11T16:01:17","guid":{"rendered":"https:\/\/networkyy.com\/filter-ai-content-rss-feeds-python\/"},"modified":"2026-09-22T09:56:54","modified_gmt":"2026-09-22T09:56:54","slug":"filter-ai-content-rss-feeds-python","status":"publish","type":"post","link":"https:\/\/networkyy.com\/fr\/filter-ai-content-rss-feeds-python\/","title":{"rendered":"Filter AI Content from RSS Feeds with Python"},"content":{"rendered":"<figure><img decoding=\"async\" src=\"https:\/\/images.pexels.com\/photos\/34803985\/pexels-photo-34803985.jpeg?auto=compress&#038;cs=tinysrgb&#038;dpr=2&#038;h=650&#038;w=940\" alt=\"Filter AI Content from RSS Feeds with Python\" style=\"width:100%;height:auto;border-radius:8px;margin-bottom:24px;\" \/><figcaption>Photo by Daniil Komov on Pexels<\/figcaption><\/figure>\n<h1>Filter AI Content from RSS Feeds with Python<\/h1>\n<p>A new experiment just hit the Hacker News frontpage: &#8220;Hacker News, without AI&#8221; \u2014 a custom feed at hcker.news that filters out AI-related stories. It&#8217;s garnered 49 points and sparked 32 comments, with developers debating whether they&#8217;re drowning in AI noise or whether the technology deserves its moment in the sun.<\/p>\n<p>Regardless of where you stand on that debate, the technical challenge behind this tool is fascinating: how do you programmatically filter content based on keywords, context, or even sentiment? Whether you want to screen out AI posts, cryptocurrency hype, or political flame wars, the underlying skill is the same. Today, we&#8217;re building a real RSS content filter in Python that you can customize for any topic you want to avoid \u2014 or exclusively surface.<\/p>\n<h2>Table of Contents<\/h2>\n<ul>\n<li><a href=\"#understanding-rss\">Understanding RSS Feeds and Why They Still Matter<\/a><\/li>\n<li><a href=\"#building-filter\">Building a Simple RSS Filter in Python<\/a><\/li>\n<li><a href=\"#advanced-filtering\">Advanced Filtering with NLP and Pattern Matching<\/a><\/li>\n<li><a href=\"#deployment\">Deploying Your Filter as a Custom Feed<\/a><\/li>\n<\/ul>\n<h2 id=\"understanding-rss\">Understanding RSS Feeds and Why They Still Matter<\/h2>\n<p>RSS might seem like a relic from the early 2000s, but it&#8217;s experiencing a quiet renaissance among professionals tired of algorithmic feeds. Hacker News itself offers RSS feeds for stories, and the &#8220;without AI&#8221; variant shows exactly why developers still value this technology: control. You decide what you consume, not an engagement-optimizing algorithm.<\/p>\n<p>For IT professionals, RSS feeds are also an automation goldmine. Every major tech blog, GitHub release page, security advisory board, and community forum offers RSS. If you can parse and filter these feeds intelligently, you&#8217;ve built yourself a custom intelligence pipeline. Many engineers pursuing deeper Python skills through platforms like <a href=\"https:\/\/datacamp.pxf.io\/YR9dQK\" target=\"_blank\" rel=\"nofollow sponsored noopener\">DataCamp<\/a> start with RSS projects because they combine web scraping, text processing, and data pipelines in one practical package.<\/p>\n<p>The basic structure of an RSS feed is XML with standardized tags: <code>&lt;item&gt;<\/code> elements contain <code>&lt;title&gt;<\/code>, <code>&lt;link&gt;<\/code>, <code>&lt;description&gt;<\/code>, and <code>&lt;pubDate&gt;<\/code>. Our job is to fetch these feeds, parse the XML, apply filtering logic, and either display or re-serve the results.<\/p>\n<h2 id=\"building-filter\">Building a Simple RSS Filter in Python<\/h2>\n<p>Let&#8217;s start with a straightforward implementation using the <code>feedparser<\/code> library, which handles the messy details of XML parsing and date formats for us. Our first filter will do exactly what the trending Hacker News clone does: remove entries containing AI-related keywords.<\/p>\n<pre><code># Simple RSS filter that excludes AI-related stories from Hacker News\nimport feedparser\n\ndef filter_rss_feed(feed_url, exclude_keywords):\n    feed = feedparser.parse(feed_url)\n    filtered_entries = []\n    \n    for entry in feed.entries:\n        title_lower = entry.title.lower()\n        summary_lower = entry.get('summary', '').lower()\n        \n        # Check if any exclude keyword appears in title or summary\n        if not any(keyword in title_lower or keyword in summary_lower \n                   for keyword in exclude_keywords):\n            filtered_entries.append(entry)\n    \n    return filtered_entries\n\n# Example: Filter Hacker News RSS\nhn_rss = \"https:\/\/news.ycombinator.com\/rss\"\nai_keywords = ['ai', 'artificial intelligence', 'llm', 'gpt', 'chatgpt', \n               'machine learning', 'ml model', 'openai']\n\nfiltered = filter_rss_feed(hn_rss, ai_keywords)\n\nfor item in filtered[:10]:\n    print(f\"{item.title}\\n{item.link}\\n\")\n<\/code><\/pre>\n<p>This script fetches the live Hacker News RSS feed and filters out any story whose title or description contains our AI-related keywords. Run it yourself and you&#8217;ll see a dramatically different view of the frontpage \u2014 exactly what the hcker.news experiment provides, but now you control the logic.<\/p>\n<div style=\"background:#fef3c7;border-left:4px solid #f59e0b;padding:14px 18px;border-radius:6px;margin:20px 0;\"><strong>\u26a0\ufe0f Common Mistake:<\/strong> Using simple substring matching like this will catch &#8220;Thailand&#8221; when you&#8217;re filtering for &#8220;AI&#8221;. Always convert to lowercase and consider word boundaries. For production use, employ regex with <code>\\bai\\b<\/code> to match whole words only.<\/div>\n<p>One reason this approach remains popular in courses on <a href=\"https:\/\/imp.i384100.net\/zxbRDr\" target=\"_blank\" rel=\"nofollow sponsored noopener\">Coursera<\/a> is that it teaches you to think about text processing as layers: fetch, parse, filter, present. Each layer can grow more sophisticated without changing the overall architecture.<\/p>\n<h2 id=\"advanced-filtering\">Advanced Filtering with NLP and Pattern Matching<\/h2>\n<p>Simple keyword matching works, but it&#8217;s brittle. What if someone writes &#8220;large language models&#8221; instead of &#8220;LLM&#8221;? What about sarcasm, or posts that mention AI only in passing? Let&#8217;s upgrade our filter with regex patterns and basic natural language processing to catch semantic variations.<\/p>\n<pre><code># Advanced RSS filter with regex and intelligent keyword matching\nimport feedparser\nimport re\nfrom collections import Counter\n\ndef advanced_filter(feed_url, pattern_list, threshold=1):\n    feed = feedparser.parse(feed_url)\n    filtered_entries = []\n    \n    # Compile regex patterns for performance\n    compiled_patterns = [re.compile(p, re.IGNORECASE) for p in pattern_list]\n    \n    for entry in feed.entries:\n        text = f\"{entry.title} {entry.get('summary', '')}\"\n        \n        # Count pattern matches\n        match_count = sum(1 for pattern in compiled_patterns if pattern.search(text))\n        \n        # Only exclude if match count exceeds threshold\n        if match_count < threshold:\n            filtered_entries.append({\n                'title': entry.title,\n                'link': entry.link,\n                'published': entry.get('published', 'Unknown date')\n            })\n    \n    return filtered_entries\n\n# More sophisticated AI detection patterns\nai_patterns = [\n    r'\\b(ai|artificial\\s+intelligence)\\b',\n    r'\\b(gpt-?\\d*|chatgpt|claude|gemini)\\b',\n    r'\\b(llm|large\\s+language\\s+model)s?\\b',\n    r'\\b(machine\\s+learning|deep\\s+learning|neural\\s+network)s?\\b',\n    r'\\b(openai|anthropic|stability\\.?ai)\\b'\n]\n\nfiltered = advanced_filter(\"https:\/\/news.ycombinator.com\/rss\", ai_patterns, threshold=2)\n\nprint(f\"Found {len(filtered)} stories without significant AI mentions:\\n\")\nfor item in filtered[:5]:\n    print(f\"\u2022 {item['title']}\")\n    print(f\"  {item['link']}\\n\")\n<\/code><\/pre>\n<p>This version uses compiled regular expressions for speed and introduces a threshold parameter. A story must match at least two patterns before being filtered out, which reduces false positives. Notice how we've also normalized the output into clean dictionaries, making this data easy to pass into a template engine or write to a database.<\/p>\n<h3>Taking It Further: Sentiment and Context<\/h3>\n<p>If you wanted to replicate the full sophistication of a service like hcker.news, you'd layer in sentiment analysis or even a lightweight classification model. Libraries like <code>textblob<\/code> or <code>transformers<\/code> can help you distinguish between a post critically analyzing AI hype versus one evangelizing a new model. But even without machine learning, smart regex and threshold-based filtering gets you 80% of the way there.<\/p>\n<div style=\"background:#fef3c7;border-left:4px solid #f59e0b;padding:14px 18px;border-radius:6px;margin:20px 0;\"><strong>\ud83d\udca1 Pro Tip:<\/strong> Store your patterns in a JSON config file instead of hardcoding them. That way you can swap between \"no AI,\" \"no crypto,\" or \"only security\" filters without touching your code.<\/div>\n<h2 id=\"deployment\">Deploying Your Filter as a Custom Feed<\/h2>\n<p>The real magic happens when you deploy this as a live RSS feed others can subscribe to \u2014 just like the trending hcker.news experiment. You'll need to generate valid RSS XML and serve it via a simple web server.<\/p>\n<p>Here's a minimal Flask app that does exactly that:<\/p>\n<pre><code># Serve a filtered RSS feed using Flask\nfrom flask import Flask, Response\nimport feedparser\nfrom datetime import datetime\n\napp = Flask(__name__)\n\n@app.route('\/feed')\ndef filtered_feed():\n    # Fetch and filter\n    feed = feedparser.parse(\"https:\/\/news.ycombinator.com\/rss\")\n    exclude = ['ai', 'gpt', 'llm']\n    \n    filtered = [e for e in feed.entries \n                if not any(kw in e.title.lower() for kw in exclude)]\n    \n    # Build RSS XML\n    rss_items = []\n    for entry in filtered[:25]:\n        item = f\"\"\"\n        <item>\n            <title>{entry.title}<\/title>\n            <link>{entry.link}<\/link>\n            <description>{entry.get('summary', '')}<\/description>\n            <pubDate>{entry.get('published', '')}<\/pubDate>\n        <\/item>\n        \"\"\"\n        rss_items.append(item)\n    \n    rss_xml = f\"\"\"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n    <rss version=\"2.0\">\n        <channel>\n            <title>Hacker News (Filtered)<\/title>\n            <link>https:\/\/news.ycombinator.com<\/link>\n            <description>HN stories, minus the noise<\/description>\n            {''.join(rss_items)}\n        <\/channel>\n    <\/rss>\n    \"\"\"\n    \n    return Response(rss_xml, mimetype='application\/rss+xml')\n\nif __name__ == '__main__':\n    app.run(debug=True)\n<\/code><\/pre>\n<p>Deploy this to a cheap VPS, a Docker container, or a serverless platform like AWS Lambda with API Gateway, and you've got your own custom feed anyone can subscribe to in their RSS reader. You could even add query parameters like <code>\/feed?exclude=ai,crypto<\/code> to make it user-configurable.<\/p>\n<p>The beauty of this approach is how extensible it becomes. Want to filter by domain? Add a whitelist check. Want to highlight security posts? Invert the logic and create an <code>include<\/code> filter instead of <code>exclude<\/code>. Once you've built the skeleton, modifications take minutes.<\/p>\n<h2>Why This Matters Beyond the Hacker News Experiment<\/h2>\n<p>The \"Hacker News, without AI\" project resonates because it solves a universal problem: information overload in a hype-driven media cycle. But the skill you've just learned \u2014 programmatic content filtering \u2014 applies far beyond one niche community.<\/p>\n<p>Security teams use RSS filters to monitor CVE feeds for specific vendors. DevOps engineers watch GitHub release feeds for breaking changes in dependencies. Product managers track competitor blogs while filtering out marketing fluff. The pattern is identical in every case: fetch, parse, filter, act.<\/p>\n<p>By mastering these fundamentals, you're not just replicating someone's weekend project. You're building a toolkit for creating custom intelligence pipelines that keep you informed without drowning you in noise. In an era where every platform wants to algorithmically curate your attention, the ability to write your own filters is a genuine superpower.<\/p>\n<div style=\"background:#f8f8f8;color:#555;padding:14px 18px;border-radius:8px;margin-top:32px;font-size:14px;line-height:1.6;\"><span style=\"color:#222;font-weight:600;\">Stay in the loop<\/span> \u2014 join 125,000+ IT professionals following Networkyy: <a href=\"https:\/\/www.instagram.com\/networkyy\" target=\"_blank\" style=\"color:#7c3aed;font-weight:600;text-decoration:none;\" rel=\"noopener\">Instagram<\/a> \u00b7 <a href=\"https:\/\/www.facebook.com\/ITnetworkyy\/\" target=\"_blank\" style=\"color:#7c3aed;font-weight:600;text-decoration:none;\" rel=\"noopener\">Facebook<\/a> \u00b7 <a href=\"https:\/\/www.threads.com\/@networkyy\" target=\"_blank\" style=\"color:#7c3aed;font-weight:600;text-decoration:none;\" rel=\"noopener\">Threads<\/a> \u00b7 <a href=\"https:\/\/medium.com\/@mattouchi6\" target=\"_blank\" style=\"color:#7c3aed;font-weight:600;text-decoration:none;\" rel=\"noopener\">Medium<\/a><\/div>\n<div style=\"background:linear-gradient(135deg,#1e1b4b,#6d28d9 55%,#db2777);border-radius:16px;padding:30px 24px;text-align:center;box-shadow:0 10px 30px rgba(109,40,217,0.35);\">\n<div style=\"display:inline-block;background:#facc15;color:#1e1b4b;font-size:11px;font-weight:800;letter-spacing:0.5px;padding:5px 12px;border-radius:999px;margin-bottom:14px;\">\ud83d\udd25 RECOMMENDED FOR YOU<\/div>\n<h3 style=\"margin:0 0 10px;font-size:20px;color:#fff;font-weight:800;line-height:1.3;\">Build Your Own Content Filters<\/h3>\n<p style=\"margin:0 0 20px;color:#e9d5ff;font-size:13.5px;line-height:1.6;\">Master web scraping, RSS parsing, and NLP fundamentals through hands-on projects that automate your information diet. Real-world Python skills that pay off immediately in DevOps, security, and data engineering roles.<\/p>\n<p><a href=\"https:\/\/datacamp.pxf.io\/YR9dQK\" target=\"_blank\" rel=\"nofollow sponsored noopener\" style=\"display:inline-block;background:#a3e635;color:#1e1b4b;font-weight:800;padding:13px 30px;border-radius:10px;font-size:14.5px;box-shadow:0 4px 14px rgba(163,230,53,0.5);text-decoration:none;\">Start Learning on DataCamp \u2192<\/a><\/div>","protected":false},"excerpt":{"rendered":"<p>Learn to build a Python filter that detects and removes AI-generated content from RSS feeds, inspired by a trending Hacker News experiment.<\/p>","protected":false},"author":2,"featured_media":803,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":"","_yoast_wpseo_title":"Filter AI Content from RSS Feeds with Python - Networkyy","_yoast_wpseo_metadesc":"Learn to build a Python filter that detects and removes AI-generated content from RSS feeds, inspired by a trending Hacker News experiment.","_yoast_wpseo_focuskw":"Python RSS filter","rank_math_title":"Filter AI Content from RSS Feeds with Python - Networkyy","rank_math_description":"Learn to build a Python filter that detects and removes AI-generated content from RSS feeds, inspired by a trending Hacker News experiment.","rank_math_focus_keyword":"Python RSS filter"},"categories":[15,11],"tags":[],"class_list":["post-804","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-and-data-science","category-python-automation"],"contentshake_article_id":"","brizy_media":[],"_links":{"self":[{"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/posts\/804","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/comments?post=804"}],"version-history":[{"count":1,"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/posts\/804\/revisions"}],"predecessor-version":[{"id":811,"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/posts\/804\/revisions\/811"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/media\/803"}],"wp:attachment":[{"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/media?parent=804"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/categories?post=804"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/networkyy.com\/fr\/wp-json\/wp\/v2\/tags?post=804"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}