Filter AI Content from RSS Feeds with Python

A new experiment just hit the Hacker News frontpage: “Hacker News, without AI” — a custom feed at hcker.news that filters out AI-related stories. It’s garnered 49 points and sparked 32 comments, with developers debating whether they’re drowning in AI noise or whether the technology deserves its moment in the sun.

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’re building a real RSS content filter in Python that you can customize for any topic you want to avoid — or exclusively surface.

Table of Contents

• Understanding RSS Feeds and Why They Still Matter
• Building a Simple RSS Filter in Python
• Advanced Filtering with NLP and Pattern Matching
• Deploying Your Filter as a Custom Feed

Understanding RSS Feeds and Why They Still Matter

RSS might seem like a relic from the early 2000s, but it’s experiencing a quiet renaissance among professionals tired of algorithmic feeds. Hacker News itself offers RSS feeds for stories, and the “without AI” variant shows exactly why developers still value this technology: control. You decide what you consume, not an engagement-optimizing algorithm.

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’ve built yourself a custom intelligence pipeline. Many engineers pursuing deeper Python skills through platforms like DataCamp start with RSS projects because they combine web scraping, text processing, and data pipelines in one practical package.

The basic structure of an RSS feed is XML with standardized tags: <item> elements contain <title>, <link>, <description>, and <pubDate>. Our job is to fetch these feeds, parse the XML, apply filtering logic, and either display or re-serve the results.

Building a Simple RSS Filter in Python

Let’s start with a straightforward implementation using the feedparser 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.

# Simple RSS filter that excludes AI-related stories from Hacker News
import feedparser

def filter_rss_feed(feed_url, exclude_keywords):
    feed = feedparser.parse(feed_url)
    filtered_entries = []
    
    for entry in feed.entries:
        title_lower = entry.title.lower()
        summary_lower = entry.get('summary', '').lower()
        
        # Check if any exclude keyword appears in title or summary
        if not any(keyword in title_lower or keyword in summary_lower 
                   for keyword in exclude_keywords):
            filtered_entries.append(entry)
    
    return filtered_entries

# Example: Filter Hacker News RSS
hn_rss = "https://news.ycombinator.com/rss"
ai_keywords = ['ai', 'artificial intelligence', 'llm', 'gpt', 'chatgpt', 
               'machine learning', 'ml model', 'openai']

filtered = filter_rss_feed(hn_rss, ai_keywords)

for item in filtered[:10]:
    print(f"{item.title}\n{item.link}\n")

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’ll see a dramatically different view of the frontpage — exactly what the hcker.news experiment provides, but now you control the logic.

One reason this approach remains popular in courses on Coursera 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.

Advanced Filtering with NLP and Pattern Matching

Simple keyword matching works, but it’s brittle. What if someone writes “large language models” instead of “LLM”? What about sarcasm, or posts that mention AI only in passing? Let’s upgrade our filter with regex patterns and basic natural language processing to catch semantic variations.

# Advanced RSS filter with regex and intelligent keyword matching
import feedparser
import re
from collections import Counter

def advanced_filter(feed_url, pattern_list, threshold=1):
    feed = feedparser.parse(feed_url)
    filtered_entries = []
    
    # Compile regex patterns for performance
    compiled_patterns = [re.compile(p, re.IGNORECASE) for p in pattern_list]
    
    for entry in feed.entries:
        text = f"{entry.title} {entry.get('summary', '')}"
        
        # Count pattern matches
        match_count = sum(1 for pattern in compiled_patterns if pattern.search(text))
        
        # Only exclude if match count exceeds threshold
        if match_count < threshold:
            filtered_entries.append({
                'title': entry.title,
                'link': entry.link,
                'published': entry.get('published', 'Unknown date')
            })
    
    return filtered_entries

# More sophisticated AI detection patterns
ai_patterns = [
    r'\b(ai|artificial\s+intelligence)\b',
    r'\b(gpt-?\d*|chatgpt|claude|gemini)\b',
    r'\b(llm|large\s+language\s+model)s?\b',
    r'\b(machine\s+learning|deep\s+learning|neural\s+network)s?\b',
    r'\b(openai|anthropic|stability\.?ai)\b'
]

filtered = advanced_filter("https://news.ycombinator.com/rss", ai_patterns, threshold=2)

print(f"Found {len(filtered)} stories without significant AI mentions:\n")
for item in filtered[:5]:
    print(f"• {item['title']}")
    print(f"  {item['link']}\n")

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.

Taking It Further: Sentiment and Context

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 textblob or transformers 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.

Deploying Your Filter as a Custom Feed

The real magic happens when you deploy this as a live RSS feed others can subscribe to — just like the trending hcker.news experiment. You'll need to generate valid RSS XML and serve it via a simple web server.

Here's a minimal Flask app that does exactly that:

# Serve a filtered RSS feed using Flask
from flask import Flask, Response
import feedparser
from datetime import datetime

app = Flask(__name__)

@app.route('/feed')
def filtered_feed():
    # Fetch and filter
    feed = feedparser.parse("https://news.ycombinator.com/rss")
    exclude = ['ai', 'gpt', 'llm']
    
    filtered = [e for e in feed.entries 
                if not any(kw in e.title.lower() for kw in exclude)]
    
    # Build RSS XML
    rss_items = []
    for entry in filtered[:25]:
        item = f"""
        
            
            {entry.link}
            {entry.get('summary', '')}
            {entry.get('published', '')}
        
        """
        rss_items.append(item)
    
    rss_xml = f"""
    
        
            
            https://news.ycombinator.com
            HN stories, minus the noise
            {''.join(rss_items)}
        
    
    """
    
    return Response(rss_xml, mimetype='application/rss+xml')

if __name__ == '__main__':
    app.run(debug=True)

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 /feed?exclude=ai,crypto to make it user-configurable.

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 include filter instead of exclude. Once you've built the skeleton, modifications take minutes.

Why This Matters Beyond the Hacker News Experiment

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 — programmatic content filtering — applies far beyond one niche community.

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.

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.