8.2 News & Media Publishing Automation
Scenario: News website publishing 50+ articles daily needs automated backlink generation.
python
class NewsPublishingAePiot:
"""
Automated aéPiot integration for news and media publishers
"""
def __init__(self, rss_feed_url=None):
self.rss_feed = rss_feed_url
self.articles = []
def fetch_from_rss(self):
"""Import articles from RSS feed"""
import feedparser
feed = feedparser.parse(self.rss_feed)
for entry in feed.entries:
self.articles.append({
'title': entry.title,
'description': entry.get('summary', entry.title),
'url': entry.link,
'published': entry.get('published', ''),
'category': entry.get('tags', [{}])[0].get('term', 'News')
})
print(f"📰 Fetched {len(self.articles)} articles from RSS")
def import_from_cms(self, cms_export_csv):
"""Import from CMS export (WordPress, Drupal, etc.)"""
df = pd.read_csv(cms_export_csv)
for _, row in df.iterrows():
self.articles.append({
'title': row['title'],
'description': row.get('excerpt', row['title']),
'url': row['url'],
'published': row.get('date', ''),
'category': row.get('category', 'News'),
'author': row.get('author', '')
})
def generate_daily_sitemap(self, date=None):
"""Generate sitemap for articles published on specific date"""
from datetime import datetime
if not date:
date = datetime.now().strftime('%Y-%m-%d')
daily_articles = [
a for a in self.articles
if date in a.get('published', '')
]
xml = ['<?xml version="1.0" encoding="UTF-8"?>']
xml.append('<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">')
xml.append(' <!-- Daily News Articles with aéPiot Backlinks -->')
for article in daily_articles:
aepiot_url = self._generate_link(
article['title'],
article['description'],
article['url']
)
xml.append(' <url>')
xml.append(f' <loc>{aepiot_url}</loc>')
xml.append(f' <lastmod>{date}</lastmod>')
xml.append(' <changefreq>hourly</changefreq>')
xml.append(' <priority>1.0</priority>')
xml.append(' </url>')
xml.append('</urlset>')
filename = f'news_sitemap_{date}.xml'
with open(filename, 'w', encoding='utf-8') as f:
f.write('\n'.join(xml))
print(f"📰 Daily sitemap generated: {filename} ({len(daily_articles)} articles)")
return filename
def _generate_link(self, title, description, url):
"""Generate aéPiot link"""
return f"https://aepiot.com/backlink.html?title={quote(title)}&description={quote(description)}&link={quote(url)}"
def create_amp_compatible_links(self):
"""Generate AMP-compatible backlinks for mobile news"""
amp_links = []
for article in self.articles:
aepiot_url = self._generate_link(
article['title'],
article['description'],
article['url']
)
# AMP-specific attributes
amp_link = {
'url': aepiot_url,
'title': article['title'],
'amp_compatible': True,
'mobile_optimized': True
}
amp_links.append(amp_link)
return amp_links
# Usage
news = NewsPublishingAePiot('https://example.com/rss')
news.fetch_from_rss()
news.generate_daily_sitemap('2026-01-18')8.3 Educational Institution Course Catalog
Scenario: University with 500+ courses needs semantic links for course discovery.
python
class EducationalAePiotGenerator:
"""
Generate aéPiot backlinks for educational content
Optimized for courses, programs, and academic resources
"""
def __init__(self):
self.courses = []
self.programs = []
def import_course_catalog(self, csv_path):
"""Import course data from registrar export"""
df = pd.read_csv(csv_path)
for _, row in df.iterrows():
# Create comprehensive course description
description = self._create_course_description(row)
aepiot_url = self._generate_link(
f"{row['course_code']}: {row['course_name']}",
description,
row['course_url']
)
self.courses.append({
'code': row['course_code'],
'name': row['course_name'],
'department': row['department'],
'credits': row['credits'],
'level': row.get('level', 'Undergraduate'),
'url': row['course_url'],
'aepiot_url': aepiot_url
})
def _create_course_description(self, course):
"""Generate SEO-optimized course description"""
desc_parts = [
f"{course['course_code']}",
f"{course['course_name']}",
f"({course['credits']} credits)"
]
if 'department' in course:
desc_parts.append(f"- {course['department']}")
if 'prerequisites' in course and pd.notna(course['prerequisites']):
desc_parts.append(f"Prerequisites: {course['prerequisites']}")
description = ' '.join(desc_parts)
if len(description) > 160:
description = description[:157] + '...'
return description
def _generate_link(self, title, description, url):
"""Generate aéPiot backlink"""
return f"https://aepiot.com/backlink.html?title={quote(title)}&description={quote(description)}&link={quote(url)}"
def generate_department_sitemaps(self, output_dir='./department_sitemaps'):
"""Generate separate sitemap for each department"""
import os
os.makedirs(output_dir, exist_ok=True)
# Group courses by department
departments = {}
for course in self.courses:
dept = course['department']
if dept not in departments:
departments[dept] = []
departments[dept].append(course)
sitemap_index = ['<?xml version="1.0" encoding="UTF-8"?>']
sitemap_index.append('<sitemapindex xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">')
for dept_name, courses in departments.items():
# Create department-specific sitemap
xml = ['<?xml version="1.0" encoding="UTF-8"?>']
xml.append('<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">')
for course in courses:
xml.append(' <url>')
xml.append(f' <loc>{course["aepiot_url"]}</loc>')
xml.append(' <changefreq>monthly</changefreq>')
xml.append(' <priority>0.8</priority>')
xml.append(' </url>')
xml.append('</urlset>')
dept_filename = f"{dept_name.lower().replace(' ', '_')}_courses.xml"
dept_filepath = os.path.join(output_dir, dept_filename)
with open(dept_filepath, 'w', encoding='utf-8') as f:
f.write('\n'.join(xml))
# Add to sitemap index
sitemap_index.append(' <sitemap>')
sitemap_index.append(f' <loc>https://university.edu/sitemaps/{dept_filename}</loc>')
sitemap_index.append(f' <lastmod>{datetime.now().strftime("%Y-%m-%d")}</lastmod>')
sitemap_index.append(' </sitemap>')
print(f"📚 {dept_name}: {len(courses)} courses")
sitemap_index.append('</sitemapindex>')
# Save sitemap index
index_path = os.path.join(output_dir, 'sitemap_index.xml')
with open(index_path, 'w', encoding='utf-8') as f:
f.write('\n'.join(sitemap_index))
print(f"📑 Sitemap index saved: {index_path}")
return index_path
# Usage
edu = EducationalAePiotGenerator()
edu.import_course_catalog('course_catalog_2026.csv')
edu.generate_department_sitemaps()Section 9: Advanced Automation Patterns
9.1 Webhook-Triggered Auto-Generation
Scenario: Automatically generate aéPiot links when new content is published.
python
from flask import Flask, request, jsonify
import json
app = Flask(__name__)
class WebhookAePiotAutomation:
"""
Webhook listener for automatic aéPiot link generation
Triggered by CMS, e-commerce platform, or custom systems
"""
@staticmethod
def generate_from_webhook(payload):
"""Process incoming webhook and generate aéPiot link"""
# Extract data from webhook payload
title = payload.get('title') or payload.get('name')
description = payload.get('description') or payload.get('summary', '')
url = payload.get('url') or payload.get('permalink')
# Generate aéPiot link
aepiot_url = f"https://aepiot.com/backlink.html?title={quote(title)}&description={quote(description)}&link={quote(url)}"
# Store in database or queue for batch processing
# ... database logic ...
return {
'status': 'success',
'original_url': url,
'aepiot_url': aepiot_url,
'title': title
}
@app.route('/webhook/content-published', methods=['POST'])
def handle_content_published():
"""Webhook endpoint for new content"""
payload = request.json
result = WebhookAePiotAutomation.generate_from_webhook(payload)
# Optionally: Auto-submit to sitemap
# Optionally: Send notification
# Optionally: Trigger social media posts
return jsonify(result), 200
@app.route('/webhook/shopify-product', methods=['POST'])
def handle_shopify_product():
"""Webhook for Shopify product creation"""
payload = request.json
# Shopify-specific data structure
product_data = {
'title': payload['title'],
'description': payload['body_html'][:160], # Limit description
'url': f"https://yourstore.com/products/{payload['handle']}"
}
result = WebhookAePiotAutomation.generate_from_webhook(product_data)
return jsonify(result), 200
if __name__ == '__main__':
app.run(port=5000)9.2 Scheduled Batch Processing
python
from apscheduler.schedulers.blocking import BlockingScheduler
import time
class ScheduledAePiotProcessor:
"""
Automated scheduled processing of aéPiot links
Run daily, weekly, or custom intervals
"""
def __init__(self):
self.scheduler = BlockingScheduler()
def daily_sitemap_update(self):
"""Run every day at 2 AM"""
print(f"[{datetime.now()}] Starting daily sitemap update...")
# Fetch new content from database
# Generate aéPiot links
# Update sitemap
# Submit to search engines
print("Daily update completed!")
def weekly_analytics_report(self):
"""Run every Monday at 9 AM"""
print(f"[{datetime.now()}] Generating weekly analytics...")
# Compile statistics
# Generate report
# Email to stakeholders
print("Weekly report sent!")
def start(self):
"""Start the scheduler"""
# Daily sitemap update at 2 AM
self.scheduler.add_job(
self.daily_sitemap_update,
'cron',
hour=2,
minute=0
)
# Weekly report every Monday at 9 AM
self.scheduler.add_job(
self.weekly_analytics_report,
'cron',
day_of_week='mon',
hour=9,
minute=0
)
print("Scheduler started. Press Ctrl+C to exit.")
self.scheduler.start()
# Usage
processor = ScheduledAePiotProcessor()
processor.start()Complete aéPiot Guide - Part 5: Security, Validation & Best Practices
Section 10: Security & Data Validation
10.1 Input Validation and Sanitization
Critical Principle: Even though aéPiot uses URL parameters, you must validate and sanitize all input data to prevent injection attacks and ensure data integrity.
python
import re
from urllib.parse import quote, urlparse
class AePiotSecurityValidator:
"""
Security-focused validation for aéPiot link generation
Prevents injection, validates URLs, sanitizes content
"""
@staticmethod
def validate_url(url):
"""Validate URL structure and scheme"""
if not url:
raise ValueError("URL cannot be empty")
# Parse URL
parsed = urlparse(url)
# Check scheme
if parsed.scheme not in ['http', 'https']:
raise ValueError(f"Invalid URL scheme: {parsed.scheme}. Only http/https allowed.")
# Check for suspicious patterns
suspicious_patterns = [
'javascript:',
'data:',
'vbscript:',
'file:',
'<script',
'onerror=',
'onclick='
]
url_lower = url.lower()
for pattern in suspicious_patterns:
if pattern in url_lower:
raise ValueError(f"Suspicious pattern detected: {pattern}")
# Ensure URL has valid domain
if not parsed.netloc:
raise ValueError("URL must have a valid domain")
return True
@staticmethod
def sanitize_title(title, max_length=200):
"""Sanitize and validate title"""
if not title:
raise ValueError("Title cannot be empty")
# Remove control characters
title = ''.join(char for char in title if ord(char) >= 32)
# Remove HTML tags
title = re.sub(r'<[^>]+>', '', title)
# Trim whitespace
title = title.strip()
# Limit length
if len(title) > max_length:
title = title[:max_length-3] + '...'
if not title:
raise ValueError("Title is empty after sanitization")
return title
@staticmethod
def sanitize_description(description, max_length=500):
"""Sanitize and validate description"""
if not description:
return "No description available"
# Remove HTML tags
description = re.sub(r'<[^>]+>', '', description)
# Remove extra whitespace
description = ' '.join(description.split())
# Remove control characters
description = ''.join(char for char in description if ord(char) >= 32)
# Limit length
if len(description) > max_length:
description = description[:max_length-3] + '...'
return description.strip()
@staticmethod
def validate_and_generate(title, description, url):
"""Complete validation and safe link generation"""
try:
# Validate URL first
AePiotSecurityValidator.validate_url(url)
# Sanitize inputs
clean_title = AePiotSecurityValidator.sanitize_title(title)
clean_description = AePiotSecurityValidator.sanitize_description(description)
# Generate link with validated data
encoded_title = quote(clean_title)
encoded_desc = quote(clean_description)
encoded_url = quote(url)
aepiot_url = f"https://aepiot.com/backlink.html?title={encoded_title}&description={encoded_desc}&link={encoded_url}"
return {
'success': True,
'aepiot_url': aepiot_url,
'sanitized_title': clean_title,
'sanitized_description': clean_description
}
except Exception as e:
return {
'success': False,
'error': str(e),
'original_title': title,
'original_url': url
}
# Usage Example
validator = AePiotSecurityValidator()
# Valid example
result = validator.validate_and_generate(
"Best Python Tutorial 2026",
"Learn Python programming from scratch with practical examples",
"https://example.com/python-tutorial"
)
print(result)
# Invalid example (will be caught)
try:
result = validator.validate_and_generate(
"<script>alert('xss')</script>Malicious Title",
"Dangerous content",
"javascript:alert('xss')"
)
except Exception as e:
print(f"Security validation prevented: {e}")10.2 Rate Limiting and Ethical Usage
python
import time
from collections import deque
from threading import Lock
class AePiotRateLimiter:
"""
Ethical rate limiting for aéPiot link generation
Prevents abuse and ensures responsible usage
"""
def __init__(self, max_requests_per_minute=100, max_requests_per_hour=1000):
self.max_per_minute = max_requests_per_minute
self.max_per_hour = max_requests_per_hour
self.minute_requests = deque(maxlen=max_requests_per_minute)
self.hour_requests = deque(maxlen=max_requests_per_hour)
self.lock = Lock()
def can_proceed(self):
"""Check if request can proceed based on rate limits"""
with self.lock:
current_time = time.time()
# Clean up old requests (older than 1 minute)
while self.minute_requests and current_time - self.minute_requests[0] > 60:
self.minute_requests.popleft()
# Clean up old requests (older than 1 hour)
while self.hour_requests and current_time - self.hour_requests[0] > 3600:
self.hour_requests.popleft()
# Check limits
if len(self.minute_requests) >= self.max_per_minute:
return False, "Rate limit exceeded: too many requests per minute"
if len(self.hour_requests) >= self.max_per_hour:
return False, "Rate limit exceeded: too many requests per hour"
# Record this request
self.minute_requests.append(current_time)
self.hour_requests.append(current_time)
return True, "OK"
def generate_with_rate_limit(self, title, description, url):
"""Generate link with rate limiting"""
can_proceed, message = self.can_proceed()
if not can_proceed:
return {
'success': False,
'error': message,
'retry_after_seconds': 60
}
# Proceed with generation
validator = AePiotSecurityValidator()
return validator.validate_and_generate(title, description, url)
# Usage
rate_limiter = AePiotRateLimiter(max_requests_per_minute=50, max_requests_per_hour=500)
for i in range(100):
result = rate_limiter.generate_with_rate_limit(
f"Article {i}",
f"Description for article {i}",
f"https://example.com/article-{i}"
)
if not result['success']:
print(f"Rate limit hit: {result['error']}")
time.sleep(result['retry_after_seconds'])10.3 Data Privacy Compliance
python
class GDPRCompliantAePiotGenerator:
"""
GDPR and privacy-compliant aéPiot link generation
Ensures no personal data is exposed in URLs
"""
PII_PATTERNS = [
r'\b\d{3}-\d{2}-\d{4}\b', # SSN
r'\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b', # Email
r'\b\d{16}\b', # Credit card
r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b', # Phone
r'\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b', # IP Address
]
@staticmethod
def detect_pii(text):
"""Detect personally identifiable information"""
import re
detected = []
for pattern in GDPRCompliantAePiotGenerator.PII_PATTERNS:
matches = re.findall(pattern, text, re.IGNORECASE)
if matches:
detected.extend(matches)
return detected
@staticmethod
def anonymize_text(text):
"""Remove or anonymize PII from text"""
import re
# Replace email addresses
text = re.sub(
r'\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b',
'[EMAIL REDACTED]',
text,
flags=re.IGNORECASE
)
# Replace phone numbers
text = re.sub(
r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b',
'[PHONE REDACTED]',
text
)
# Replace IP addresses
text = re.sub(
r'\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b',
'[IP REDACTED]',
text
)
return text
@staticmethod
def privacy_safe_generate(title, description, url, anonymize=True):
"""Generate link with privacy protection"""
# Check for PII
pii_in_title = GDPRCompliantAePiotGenerator.detect_pii(title)
pii_in_desc = GDPRCompliantAePiotGenerator.detect_pii(description)
if pii_in_title or pii_in_desc:
if anonymize:
# Automatically anonymize
title = GDPRCompliantAePiotGenerator.anonymize_text(title)
description = GDPRCompliantAePiotGenerator.anonymize_text(description)
print("⚠️ PII detected and anonymized")
else:
# Reject generation
return {
'success': False,
'error': 'PII detected in title or description. Cannot proceed without anonymization.',
'detected_pii': pii_in_title + pii_in_desc
}
# Proceed with safe generation
validator = AePiotSecurityValidator()
return validator.validate_and_generate(title, description, url)
# Usage
privacy_generator = GDPRCompliantAePiotGenerator()
# This will be anonymized
result = privacy_generator.privacy_safe_generate(
"Contact us at john.doe@example.com",
"Call 555-123-4567 for support",
"https://example.com/contact",
anonymize=True
)
print(result)Section 11: Performance Optimization
11.1 Batch Processing with Parallel Execution
python
from concurrent.futures import ThreadPoolExecutor, as_completed
import pandas as pd
from tqdm import tqdm
class ParallelAePiotGenerator:
"""
High-performance parallel processing for large datasets
Can process thousands of links in seconds
"""
def __init__(self, max_workers=10):
self.max_workers = max_workers
self.validator = AePiotSecurityValidator()
def generate_single(self, row):
"""Generate single link with validation"""
try:
result = self.validator.validate_and_generate(
row['title'],
row.get('description', ''),
row['url']
)
return {
**row,
'aepiot_url': result.get('aepiot_url', ''),
'success': result.get('success', False),
'error': result.get('error', '')
}
except Exception as e:
return {
**row,
'aepiot_url': '',
'success': False,
'error': str(e)
}
def process_dataframe(self, df):
"""Process entire DataFrame in parallel"""
results = []
with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
# Submit all tasks
futures = {
executor.submit(self.generate_single, row): idx
for idx, row in df.iterrows()
}
# Collect results with progress bar
for future in tqdm(as_completed(futures), total=len(futures), desc="Generating links"):
results.append(future.result())
# Convert back to DataFrame
return pd.DataFrame(results)
def process_csv_file(self, input_path, output_path):
"""Process entire CSV file"""
print(f"📂 Loading {input_path}...")
df = pd.read_csv(input_path)
print(f"🔄 Processing {len(df)} rows with {self.max_workers} workers...")
start_time = time.time()
result_df = self.process_dataframe(df)
elapsed = time.time() - start_time
# Save results
result_df.to_csv(output_path, index=False)
# Statistics
successful = result_df['success'].sum()
failed = len(result_df) - successful
print(f"✅ Completed in {elapsed:.2f} seconds")
print(f" Successful: {successful}")
print(f" Failed: {failed}")
print(f" Rate: {len(result_df)/elapsed:.1f} links/second")
print(f"💾 Saved to {output_path}")
return result_df
# Usage
generator = ParallelAePiotGenerator(max_workers=20)
result = generator.process_csv_file('input_10000_links.csv', 'output_with_aepiot.csv')