Top 10 LinkedIn and Social Syndication Workflows for Senior Engineers to Minimize Server Costs and Load Overhead
1. Scheduled Content Mirroring to LinkedIn via API (PHP)
Instead of real-time posting, which can spike server load, implement a scheduled job that mirrors your latest blog posts or product updates to LinkedIn. This workflow leverages the LinkedIn Marketing API to publish content programmatically, reducing manual intervention and allowing for batch processing during off-peak hours.
First, obtain an access token from LinkedIn. This typically involves registering an application on the LinkedIn Developer portal and going through an OAuth 2.0 flow. For simplicity in this example, we’ll assume you have a valid access token stored securely.
Prerequisites
- A LinkedIn Developer App with `r_liteprofile` and `w_member_social` permissions.
- A valid OAuth 2.0 Access Token.
- PHP cURL extension enabled.
PHP Script for Posting
This script fetches the latest article from your CMS (simulated here) and posts it to LinkedIn.
<?php
// Configuration
$accessToken = 'YOUR_LINKEDIN_ACCESS_TOKEN'; // Store this securely!
$apiUrl = 'https://api.linkedin.com/v2/ugcPosts';
// Simulate fetching latest article data
$articleTitle = "Advanced Caching Strategies for E-commerce";
$articleUrl = "https://yourdomain.com/blog/advanced-caching";
$articleDescription = "A deep dive into Redis, Varnish, and CDN configurations for optimal performance.";
// Construct the LinkedIn API payload
$postData = [
'author' => 'urn:li:person:YOUR_LINKEDIN_MEMBER_ID', // Replace with your LinkedIn Member ID URN
'lifecycleState' => 'PUBLISHED',
'specificContent' => [
'com.linkedin.ugc.ShareContent' => [
'shareCommentary' => [
'text' => "🚀 New Article: {$articleTitle}\n\n{$articleDescription}\n\nRead more: {$articleUrl} #ecommerce #performance #caching"
],
'shareMediaCategory' => 'ARTICLE',
'media' => [
[
'status' => 'READY',
'originalUrl' => $articleUrl,
'title' => [
'text' => $articleTitle,
'language' => 'en-US'
]
]
]
]
],
'visibility' => [
'com.linkedin.ugc.MemberNetworkVisibility' => 'PUBLIC'
]
];
$jsonData = json_encode($postData);
// Initialize cURL
$ch = curl_init($apiUrl);
// Set cURL options
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, $jsonData);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, [
'Authorization: Bearer ' . $accessToken,
'Content-Type: application/json',
'X-Restli-Protocol-Version: 2.0.0' // Required for LinkedIn API v2
]);
// Execute cURL request
$response = curl_exec($ch);
$httpCode = curl_getinfo($ch, CURLINFO_HTTP_CODE);
// Handle response
if ($response === false) {
echo 'cURL Error: ' . curl_error($ch);
} else {
echo "HTTP Status Code: {$httpCode}\n";
echo "Response: " . $response . "\n";
}
// Close cURL session
curl_close($ch);
?>
To automate this, schedule this PHP script using cron. For example, to run daily at 3 AM:
0 3 * * * /usr/bin/php /path/to/your/linkedin_poster.php >> /var/log/linkedin_poster.log 2>&1
2. Content Aggregation and Pre-computation for Twitter Threads
Manually crafting Twitter threads is time-consuming and can lead to inconsistent posting. This workflow involves a backend process that aggregates content from various sources (blog, product updates, curated links) and pre-formats them into Twitter-ready threads. This pre-computation reduces the immediate server load when a thread is finally published.
Workflow Overview
- Content Ingestion: Regularly fetch new content from your CMS, RSS feeds, or internal APIs.
- Thread Segmentation: Break down longer content into tweet-sized chunks (max 280 characters per tweet).
- Metadata Enrichment: Add relevant hashtags, mentions, and potentially images/GIFs.
- Storage: Store these pre-formatted threads in a database or cache, tagged with a publication timestamp.
- Publication: A separate, lightweight script or service polls for threads ready for publication and uses the Twitter API to post them.
Python Script for Thread Generation
This Python script demonstrates segmenting a blog post into a Twitter thread. It uses the `tweepy` library for Twitter API interaction (though this example focuses on generation, not direct posting).
import tweepy
import textwrap
# --- Configuration ---
# Twitter API credentials (replace with your actual keys)
# CONSUMER_KEY = "YOUR_CONSUMER_KEY"
# CONSUMER_SECRET = "YOUR_CONSUMER_SECRET"
# ACCESS_TOKEN = "YOUR_ACCESS_TOKEN"
# ACCESS_TOKEN_SECRET = "YOUR_ACCESS_TOKEN_SECRET"
# Max characters per tweet, accounting for potential URL shortening
MAX_TWEET_LENGTH = 270 # Leave room for t.co shortening
# --- Simulate Content Fetching ---
def get_latest_blog_post():
return {
"title": "Optimizing Database Queries for High-Traffic E-commerce Sites",
"url": "https://yourdomain.com/blog/db-query-optimization",
"content": "Database performance is critical for e-commerce. Slow queries lead to lost sales and poor user experience. This article explores advanced techniques including indexing strategies, query plan analysis, connection pooling, and read replicas. We'll cover how to identify bottlenecks using tools like EXPLAIN and pg_stat_statements. Proper optimization can drastically reduce latency and improve overall site responsiveness, especially during peak traffic periods. Consider denormalization for specific read-heavy scenarios and leverage caching layers effectively. Regular performance audits are essential."
}
# --- Thread Generation Logic ---
def create_twitter_thread(post):
title = post['title']
url = post['url']
content = post['content']
# Initial tweet with title and URL
initial_tweet_text = f"🧵 New Blog Post: {title}\n\n{url}\n\n"
thread_tweets = []
current_text = initial_tweet_text
# Wrap the main content
wrapped_content = textwrap.wrap(content, width=MAX_TWEET_LENGTH)
for i, line in enumerate(wrapped_content):
# Check if adding the next line exceeds the limit
if len(current_text) + len(line) + 3 <= MAX_TWEET_LENGTH: # +3 for " \n" or " \n\n"
current_text += line + "\n"
else:
# Finalize the current tweet and start a new one
thread_tweets.append(current_text.strip())
current_text = line + "\n"
# Add the last tweet if there's remaining content
if current_text.strip():
thread_tweets.append(current_text.strip())
# Add tweet numbering
final_thread = []
for i, tweet_text in enumerate(thread_tweets):
if i == 0: # First tweet might already have intro text
# Ensure the first tweet doesn't exceed limit after adding numbering
prefix = f"1/{len(thread_tweets)} "
if len(prefix) + len(tweet_text) <= MAX_TWEET_LENGTH:
final_thread.append(prefix + tweet_text)
else: # If even the first tweet with numbering is too long, truncate or handle differently
final_thread.append(prefix + tweet_text[:MAX_TWEET_LENGTH - len(prefix)])
else:
prefix = f"{i+1}/{len(thread_tweets)} "
if len(prefix) + len(tweet_text) <= MAX_TWEET_LENGTH:
final_thread.append(prefix + tweet_text)
else:
final_thread.append(prefix + tweet_text[:MAX_TWEET_LENGTH - len(prefix)])
return final_thread
# --- Main Execution ---
if __name__ == "__main__":
blog_post = get_latest_blog_post()
thread = create_twitter_thread(blog_post)
print("--- Generated Twitter Thread ---")
for i, tweet in enumerate(thread):
print(f"Tweet {i+1}:\n{tweet}\n---")
# --- Placeholder for Twitter API Posting ---
# auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
# auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
# api = tweepy.API(auth)
#
# try:
# # Post the first tweet, get its ID
# first_tweet_id = api.update_status(status=thread[0]).id
# print(f"Posted first tweet with ID: {first_tweet_id}")
#
# # Post subsequent tweets, replying to the previous one
# for tweet_text in thread[1:]:
# reply_tweet_id = api.update_status(
# status=tweet_text,
# in_reply_to_status_id=first_tweet_id,
# auto_populate_reply_metadata=True
# ).id
# first_tweet_id = reply_tweet_id # Update for the next reply
# print(f"Posted reply tweet with ID: {reply_tweet_id}")
#
# except tweepy.TweepyException as e:
# print(f"Error posting to Twitter: {e}")
This generated thread can then be stored in a Redis cache with a timestamp. A separate, low-resource cron job or a lightweight microservice can poll Redis for threads ready to be published and use the Twitter API (via `tweepy` or direct HTTP requests) to post them. This decouples content generation from the actual posting, minimizing server load spikes.
3. RSS Feed Syndication to Multiple Platforms (Bash/Shell)
Leverage RSS feeds as a central hub for your content. Instead of pushing content to each platform individually, set up a system that monitors your primary RSS feed and syndicates new entries to other social networks or content aggregators. This is particularly effective for platforms that support RSS input or have simple webhook integrations.
Workflow: RSS to Zapier/IFTTT Webhooks
This approach uses a simple shell script to check for new items in an RSS feed and trigger webhooks via services like Zapier or IFTTT. These services then handle the distribution to platforms like Facebook Pages, Slack channels, or even Pinterest.
Shell Script for RSS Monitoring
#!/bin/bash
# Configuration
RSS_FEED_URL="https://yourdomain.com/blog/rss.xml"
LAST_ITEM_FILE="/tmp/last_rss_item_guid.txt"
ZAPIER_WEBHOOK_URL="https://hooks.zapier.com/hooks/catch/YOUR_ZAPIER_HOOK_ID/" # Replace with your actual webhook URL
# Function to get the latest item's GUID from RSS
get_latest_guid() {
curl -s "$RSS_FEED_URL" | xmllint --xpath 'string(//item[1]/guid)' - 2>/dev/null
}
# Function to get the latest item's title and link
get_latest_item_details() {
local guid="$1"
# Extract title and link for the specific GUID (more robust than just first item)
# This requires a more advanced xmllint or using a proper XML parser like xmlstarlet
# For simplicity, we'll assume the first item is the latest and extract its details
curl -s "$RSS_FEED_URL" | xmllint --xpath '//item[guid="'"$guid"'" or position()=1]' - 2>/dev/null | {
read -r -d '' title <<< $(xmllint --xpath 'string(//title)' -)
read -r -d '' link <<< $(xmllint --xpath 'string(//link)' -)
echo "$title|$link"
}
}
# Function to send data to Zapier webhook
send_to_zapier() {
local title="$1"
local link="$2"
local payload="{\"title\": \"$title\", \"link\": \"$link\"}"
curl -s -X POST -H "Content-Type: application/json" -d "$payload" "$ZAPIER_WEBHOOK_URL"
}
# --- Main Logic ---
# Get the GUID of the latest item
LATEST_GUID=$(get_latest_guid)
if [ -z "$LATEST_GUID" ]; then
echo "Error: Could not retrieve latest GUID from RSS feed."
exit 1
fi
# Read the last processed GUID
if [ -f "$LAST_ITEM_FILE" ]; then
LAST_PROCESSED_GUID=$(cat "$LAST_ITEM_FILE")
else
LAST_PROCESSED_GUID=""
fi
# Compare GUIDs
if [ "$LATEST_GUID" != "$LAST_PROCESSED_GUID" ]; then
echo "New item detected: $LATEST_GUID"
# Get title and link for the new item
ITEM_DETAILS=$(get_latest_item_details "$LATEST_GUID")
ITEM_TITLE=$(echo "$ITEM_DETAILS" | cut -d'|' -f1)
ITEM_LINK=$(echo "$ITEM_DETAILS" | cut -d'|' -f2)
if [ -n "$ITEM_TITLE" ] && [ -n "$ITEM_LINK" ]; then
echo "Sending to Zapier: Title='$ITEM_TITLE', Link='$ITEM_LINK'"
send_to_zapier "$ITEM_TITLE" "$ITEM_LINK"
# Update the last processed GUID file
echo "$LATEST_GUID" > "$LAST_ITEM_FILE"
echo "Updated last processed GUID to $LATEST_GUID"
else
echo "Error: Could not extract title/link for GUID $LATEST_GUID."
fi
else
echo "No new items detected."
fi
exit 0
Schedule this script using cron to run every 5-15 minutes. This ensures timely syndication without constant polling or high server load. The `xmllint` command is used for basic XML parsing; for more complex feeds or robust error handling, consider using `xmlstarlet` or a dedicated RSS parsing library in a scripting language like Python.
4. Content Archiving and Re-syndication Strategy
Periodically re-sharing evergreen content is a cost-effective SEO and engagement strategy. Instead of manually finding and re-posting, automate the process. This workflow involves identifying older, high-performing content, slightly re-packaging it (e.g., updating the intro/outro, adding a new statistic), and scheduling its re-syndication.
Automated Content Repurposing Pipeline
- Content Identification: A script (e.g., Python) queries your CMS or analytics to find posts with high engagement metrics (views, shares) older than X months.
- Content Refresh: A separate process (manual or semi-automated) updates the identified content. This could involve adding a note like “Updated [Date]” and a new paragraph or statistic.
- Scheduling Re-syndication: The updated content’s URL is added to a queue. A scheduler then uses the API methods described in points 1 and 2 to post it to LinkedIn, Twitter, etc., with a modified caption indicating it’s a “refreshed” or “updated” piece.
Python Script for Content Identification
import requests
from datetime import datetime, timedelta
import json
# --- Configuration ---
CMS_API_ENDPOINT = "https://yourdomain.com/api/v1/posts" # Example CMS API
API_KEY = "YOUR_CMS_API_KEY" # If required
LOOKBACK_MONTHS = 6
MIN_ENGAGEMENT_SCORE = 100 # Example metric (e.g., sum of views + shares)
# --- Simulate fetching posts ---
def fetch_posts_from_cms():
headers = {"Authorization": f"Bearer {API_KEY}"} if API_KEY else {}
try:
response = requests.get(CMS_API_ENDPOINT, headers=headers, params={"limit": 1000}) # Fetch a reasonable number
response.raise_for_status() # Raise an exception for bad status codes
return response.json()
except requests.exceptions.RequestException as e:
print(f"Error fetching posts from CMS: {e}")
return []
# --- Main Logic ---
if __name__ == "__main__":
all_posts = fetch_posts_from_cms()
if not all_posts:
print("No posts fetched or an error occurred.")
exit()
# Calculate the date threshold
date_threshold = datetime.now() - timedelta(days=LOOKBACK_MONTHS * 30) # Approximate
content_to_refresh = []
for post in all_posts:
try:
# Assuming post structure includes 'published_at', 'views', 'shares', 'url', 'title'
published_date = datetime.fromisoformat(post['published_at'].replace('Z', '+00:00'))
engagement = post.get('views', 0) + post.get('shares', 0) # Example engagement calculation
if published_date < date_threshold and engagement >= MIN_ENGAGEMENT_SCORE:
content_to_refresh.append({
"title": post['title'],
"url": post['url'],
"published_at": post['published_at'],
"engagement": engagement
})
except (KeyError, ValueError) as e:
print(f"Skipping post due to missing/invalid data: {post.get('id', 'N/A')} - {e}")
continue
# Sort by engagement (descending) to prioritize high-impact content
content_to_refresh.sort(key=lambda x: x['engagement'], reverse=True)
print(f"--- Found {len(content_to_refresh)} pieces of content for potential re-syndication ---")
for item in content_to_refresh[:5]: # Print top 5 as example
print(f"- Title: {item['title']}")
print(f" URL: {item['url']}")
print(f" Published: {item['published_at']}")
print(f" Engagement Score: {item['engagement']}")
print("-" * 20)
# --- Next Steps ---
# 1. Manually review this list.
# 2. Update the content on your CMS.
# 3. Add the updated URLs to a queue for automated re-syndication (e.g., using the scripts from point 1 or 2).
# You might store these in a database table like `resyndicate_queue` with a `scheduled_at` timestamp.
The output of this script serves as a curated list for your content team. Once content is updated, its URL can be added to a database table (e.g., `resyndicate_queue`) which is then processed by the posting scripts. This ensures consistent visibility for your best content without constant manual effort.
5. User-Generated Content (UGC) Aggregation via Webhooks
Encourage users to share their experiences with your product or service. Implement a system where users can submit reviews, testimonials, or case studies directly through a form on your site. Upon submission, trigger a webhook that sends this data to a central processing point.
Workflow: Form Submission -> Webhook -> Social Media
- Frontend Form: A simple HTML form on your website collects user testimonials (text, optional image/video URL).
- Backend Endpoint: A lightweight API endpoint (e.g., using Flask/Python or a serverless function) receives the form data.
- Webhook Trigger: Upon successful validation, this endpoint sends the data to a webhook URL (e.g., Zapier, Make.com, or a custom endpoint).
- Social Media Posting: The webhook service then formats and posts the UGC to platforms like LinkedIn or Twitter, potentially tagging the user if they provide their social handle.
Python Flask Example for Webhook Endpoint
from flask import Flask, request, jsonify
import requests
import hmac
import hashlib
import os
app = Flask(__name__)
# --- Configuration ---
# Use environment variables for sensitive data
WEBHOOK_SECRET = os.environ.get("WEBHOOK_SECRET") # For verifying incoming requests if needed
TARGET_WEBHOOK_URL = os.environ.get("TARGET_WEBHOOK_URL") # e.g., Zapier webhook URL
# --- Helper function to verify signature (optional but recommended) ---
def verify_signature(req):
if not WEBHOOK_SECRET:
return True # Skip verification if secret is not set
signature = req.headers.get('X-Hub-Signature')
if not signature:
return False
# Signature format is typically 'sha1=...' or 'sha256=...'
try:
method, hash_val = signature.split('=', 1)
if method not in ('sha1', 'sha256'):
return False
hasher = hmac.new(WEBHOOK_SECRET.encode('utf-8'), req.data, getattr(hashlib, method))
return hmac.compare_digest(hasher.hexdigest(), hash_val)
except ValueError:
return False
# --- Endpoint to receive UGC ---
@app.route('/webhook/ugc', methods=['POST'])
def handle_ugc_webhook():
# Optional: Verify the incoming request signature
# if not verify_signature(request):
# return jsonify({"status": "error", "message": "Invalid signature"}), 401
data = request.get_json()
if not data:
return jsonify({"status": "error", "message": "Invalid JSON payload"}), 400
# Basic validation
required_fields = ['name', 'testimonial', 'platform']
if not all(field in data for field in required_fields):
return jsonify({"status": "error", "message": "Missing required fields"}), 400
# Prepare data for the target webhook (e.g., Zapier)
# Customize this payload based on your Zapier/Make.com setup
payload = {
"user_name": data.get('name'),
"testimonial_text": data.get('testimonial'),
"source_platform": data.get('platform'),
"user_handle": data.get('handle', ''), # Optional user social handle
"image_url": data.get('image_url', '') # Optional image URL
}
try:
response = requests.post(TARGET_WEBHOOK_URL, json=payload, timeout=10)
response.raise_for_status()
app.logger.info(f"Successfully forwarded UGC to target webhook. Status: {response.status_code}")
return jsonify({"status": "success", "message": "UGC received and forwarded"}), 200
except requests.exceptions.RequestException as e:
app.logger.error(f"Error forwarding UGC to target webhook: {e}")
return jsonify({"status": "error", "message": "Failed to forward UGC"}), 500
if __name__ == '__main__':
# For development:
# app.run(debug=True, port=5000)
# For production, use a proper WSGI server like Gunicorn:
# gunicorn -w 4 -b 0.0.0.0:5000 your_flask_app:app
# Example of setting environment variables (replace with your actual values)
# export WEBHOOK_SECRET="your_super_secret_key"
# export TARGET_WEBHOOK_URL="https://hooks.zapier.com/hooks/catch/..."
# This __main__ block is typically for running the Flask dev server.
# In production, you'd run this via Gunicorn or similar.
pass
This setup offloads the task of posting to external services, minimizing your server’s direct interaction with social media APIs and reducing potential rate limiting issues or load spikes. Ensure your form submissions are validated server-side before sending to the webhook.
6. Scheduled LinkedIn Article Publishing
LinkedIn Articles offer a more in-depth publishing platform than standard posts. Automating the publishing of articles, similar to blog posts, allows for batch processing. This workflow focuses on using the LinkedIn API to schedule articles for future publication.
API Endpoint for Article Scheduling
LinkedIn’s API allows for creating articles. While direct scheduling isn’t explicitly exposed in all endpoints, you can create the article content and set its `publishedAt` timestamp. The API will then handle the publication at the specified time. This requires careful handling of the article’s structure, including HTML content.
<?php
// Configuration
$accessToken = 'YOUR_LINKEDIN_ACCESS_TOKEN'; // Store securely
$memberIdUrn = 'urn:li:person:YOUR_LINKEDIN_MEMBER_ID'; // Your LinkedIn member URN
// Article details
$articleTitle = "The Future of E-commerce Logistics";
$articleContentHtml = "<p>This article explores emerging trends in e-commerce logistics, including drone delivery, autonomous vehicles, and AI-powered inventory management. We'll discuss the impact on supply chains and customer expectations.</p><p>Key areas covered:</p><ul><li>Last-mile delivery innovations</li><li>Warehouse automation</li><li>Predictive analytics for demand forecasting</li></ul><p>The integration of these technologies promises greater efficiency and reduced costs.</p>";
$publishTimestamp = time() + (7 * 24 * 60 * 60); // Schedule for 7 days from now (in seconds)
// Construct the API payload
$postData = [
'author' => $memberIdUrn,
'content' => [
'article' => [
'title' => $articleTitle,
'content' => [
'text' => $articleContentHtml,
'format' => 'HTML'
]
]
],
'original' => 'https://yourdomain.com/blog/article-on-logistics', // Optional original URL
'publishedAt' => $publishTimestamp * 1000, // LinkedIn expects milliseconds
'visibility' => [
'com.linkedin.ugc.MemberNetworkVisibility' => 'PUBLIC'
]
];
$jsonData = json_encode($postData);
$apiUrl = 'https://api.linkedin.com/v2/articles';
// Initialize cURL
$ch = curl_init($apiUrl);
// Set cURL options
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, $jsonData);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, [
'Authorization: Bearer ' . $accessToken,
'Content-Type: application/json',
'X-Restli-Protocol-Version: 2.0.0'
]);
// Execute cURL request
$response = curl_exec($ch);
$httpCode = curl_getinfo($ch, CURLINFO_HTTP_CODE);
// Handle response
if ($response === false) {
echo 'cURL Error: ' . curl_error($ch);
} else {
echo "HTTP Status Code: {$httpCode}\n";
echo "Response: " . $response . "\n";
}
// Close cURL session
curl_close($ch);
?>
Schedule this script to run periodically (e.g., daily). It will check for new articles ready to be published and submit them to LinkedIn. This prevents a large batch of articles from being published simultaneously, smoothing out server load.
7. Automated Cross-Platform Content Summarization
Long-form content can be adapted for different platforms. Instead of manual summarization, use AI/ML services (like OpenAI’s GPT, Google’s AI Platform) to generate concise summaries suitable for Twitter, LinkedIn updates, or even short snippets for Instagram captions. This reduces the manual effort and server load associated with content adaptation.
Python Script using OpenAI API
import openai
import requests
import os
import json
# --- Configuration ---
openai.api_key = os.environ.get("OPENAI_API_KEY")
if not openai.api_key:
raise ValueError("OPENAI_API_KEY environment variable not set.")
# --- Simulate fetching long-form content ---
def get_long_form_content(url):
try:
response = requests.get(url, timeout=10)
response.raise_for_status()
# In a real scenario, you'd parse the HTML to extract main content
# For this example, we'll use a placeholder string
return "This is a very long article about the benefits of cloud computing for e-commerce businesses. It covers scalability, cost-effectiveness, security enhancements, and disaster recovery capabilities. Cloud platforms allow businesses to scale their infrastructure up or down based on demand, pay only for what they use, and benefit from advanced security measures implemented by cloud providers. Disaster recovery is also simplified, ensuring business continuity. The article provides case studies of successful cloud migrations by major e-commerce players."
except requests.exceptions.RequestException as e:
print(f"Error fetching content from {url}: {e}")
return None
# --- Function to generate summary using OpenAI ---
def generate_summary(text, platform="twitter"):
prompt = f"Summarize the following text for a {platform} post. Keep it concise and engaging. Include relevant hashtags if appropriate.\n\nText: {text}\n\nSummary:"
try:
response = openai.Completion.create(
engine="text-davinci-003", # Or a newer model like gpt-3.5-turbo via ChatCompletion
prompt=prompt,
max_tokens=150, # Adjust based on platform requirements
n=1,
stop=None,
temperature=0.7,
)
summary = response.choices[0].text.strip()
return summary
except openai.error.OpenAIError as e:
print(f"OpenAI API error: {e}")
return None
# --- Main Execution ---
if __name__ == "__main__":
article_url = "https://yourdomain.com/blog/cloud-computing-ecommerce"
long_content = get_long_form_content(article_url)
if long_content:
# Generate summary for Twitter
twitter_summary = generate_summary(long_content, platform="twitter")
if twitter_summary:
print("--- Twitter Summary ---")
print(twitter_summary)
print("-" * 20)
# Generate summary for LinkedIn
linkedin