How Smart Bloggers Are Letting AI Do 90% of the Work in 2025

import requests
import pandas as pd
import os

# Assuming you have API keys set as environment variables
GOOGLE_TRENDS_API_KEY = os.getenv('GOOGLE_TRENDS_API_KEY') # Placeholder: real GT API is more complex, often programmatic scraping or third-party
SERPAPI_KEY = os.getenv('SERPAPI_KEY')

def get_trending_topics(niche_keyword="AI automation"):
    # In 2025, we'd have direct programmatic access or sophisticated scraping.
    # For demonstration, let's simulate. Real implementation might use a proxy service or dedicated API.
    print(f"Fetching trending topics for '{niche_keyword}'...")
    # Simulate a call to a hypothetical Google Trends-like API
    trends_data = [
        {"topic": "GPT-5 fine-tuning", "volume_change": "+25%"},
        {"topic": "LangChain agents in production", "volume_change": "+18%"},
        {"topic": "Serverless AI deployments", "volume_change": "+15%"}
    ]
    return trends_data

def analyze_serp(query):
    url = f"https://serpapi.com/search?engine=google&q={query}&api_key={SERPAPI_KEY}"
    response = requests.get(url)
    data = response.json()

    results = []
    if 'organic_results' in data:
        for item in data['organic_results'][:5]: # Analyze top 5
            results.append({
                "title": item.get('title'),
                "link": item.get('link'),
                "snippet": item.get('snippet')
            })
    return results

if __name__ == "__main__":
    target_niche = "AI blogging"
    trends = get_trending_topics(target_niche)
    
    print("\nIdentified Trending Topics:")
    for trend in trends:
        print(f"- {trend['topic']} ({trend['volume_change']})")
        
        # Now, use a trending topic for SERP analysis
        print(f"  Analyzing SERP for: '{trend['topic']}'")
        serp_results = analyze_serp(trend['topic'])
        for i, result in enumerate(serp_results):
            print(f"    {i+1}. Title: {result['title']}")
            print(f"       Snippet: {result['snippet'][:100]}...") # Truncate for brevity
        print("-" * 30)

# Assuming you have a configured LangChain client or direct GPT-5 API access
import os
from openai import OpenAI # Or a hypothetical GPT-5 client in 2025

# Placeholder for your GPT-5 API key
GPT5_API_KEY = os.getenv('GPT5_API_KEY')
client = OpenAI(api_key=GPT5_API_KEY) # Replace with actual GPT-5 client if different

def generate_content_brief(topic, serp_data):
    """
    Generates a detailed content brief using GPT-5 based on a topic and SERP analysis.
    """
    serp_summary = "\n".join([f"- {item['title']}: {item['snippet']}" for item in serp_data])
    
    prompt = f"""
    You are an expert SEO content strategist. Your task is to create a detailed content brief for a blog post on the topic: "{topic}".
    
    Here is a summary of top-ranking articles for this query:
    {serp_summary}
    
    Based on this, generate a comprehensive content brief that includes:
    1.  **Target Audience**: Who is this article for?
    2.  **Primary Goal**: What should readers learn or do?
    3.  **Key Keywords**: List 3-5 primary and secondary keywords.
    4.  **Article Structure (Headings)**: Provide a logical H1, H2s, and H3s for a 1500-2000 word article. Ensure thorough coverage.
    5.  **Key Points/Concepts to Cover under each heading**: Bullet points for crucial information.
    6.  **Unique Angle/Value Proposition**: How can this article stand out from competitors?
    7.  **Call to Action (CTA)**: Suggest a clear CTA.
    
    Format the output as Markdown.
    """
    
    response = client.chat.completions.create(
        model="gpt-5-turbo", # Hypothetical GPT-5 model name
        messages=[
            {"role": "system", "content": "You are a highly skilled content strategist."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.7,
        max_tokens=1500
    )
    
    return response.choices[0].message.content

if __name__ == "__main__":
    # Example using a previously identified trending topic
    sample_topic = "GPT-5 fine-tuning"
    sample_serp_data = [
        {"title": "The Ultimate Guide to GPT-5 Fine-Tuning - AI Blog 2025", "snippet": "Learn how to fine-tune GPT-5 models for specific tasks, from data preparation to deployment..."},
        {"title": "Mastering GPT-5: Advanced Fine-Tuning Techniques for Custom AI", "snippet": "Dive deep into the latest fine-tuning methods for OpenAI's GPT-5, improving performance on niche datasets..."},
    ]
    
    print(f"\nGenerating content brief for '{sample_topic}'...")
    brief = generate_content_brief(sample_topic, sample_serp_data)
    print(brief)

import os
from openai import OpenAI

GPT5_API_KEY = os.getenv('GPT5_API_KEY')
client = OpenAI(api_key=GPT5_API_KEY)

def generate_article_draft(content_brief_markdown):
    """
    Generates a full article draft based on the content brief using GPT-5.
    """
    
    prompt = f"""
    You are an expert technical blogger and content creator. Your task is to write a comprehensive, engaging, and SEO-optimized blog post based on the following detailed content brief.
    
    Adhere strictly to the provided structure (H1, H2s, H3s) and ensure all "Key Points/Concepts to Cover" are addressed.
    Maintain a clear, energetic, and smart tone, suitable for solo content creators, bloggers, and developers.
    Integrate the "Key Keywords" naturally throughout the text.
    Aim for a detailed, high-quality draft of approximately 1500-2000 words.
    
    ---
    {content_brief_markdown}
    ---
    
    Begin writing the full article now.
    """
    
    response = client.chat.completions.create(
        model="gpt-5-turbo", # Hypothetical GPT-5 model name
        messages=[
            {"role": "system", "content": "You are a highly skilled technical blogger."},
            {"role_individual": "user", "content": prompt}
        ],
        temperature=0.7, # A bit more creative while staying factual
        max_tokens=3000 # Allow for a longer article
    )
    
    return response.choices[0].message.content

if __name__ == "__main__":
    # Assume 'brief' variable holds the markdown output from the previous step
    # For demonstration, let's use a truncated version of the brief
    example_brief_markdown = """
# Content Brief: GPT-5 Fine-Tuning

## 1. Target Audience
*   AI/ML Developers and Engineers

## 3. Key Keywords
*   Primary: "GPT-5 fine-tuning"

## 4. Article Structure (Headings)

### H1: The Ultimate Guide to GPT-5 Fine-Tuning: Master Custom LLMs in 2025

### H2: Introduction to Fine-Tuning GPT-5
*   What is fine-tuning and why is it essential for GPT-5?

### H2: Preparing Your Data for GPT-5 Fine-Tuning
*   Data collection and curation strategies

### H2: Choosing the Right Fine-Tuning Approach
*   Full fine-tuning vs. Parameter-Efficient Fine-Tuning (PEFT)

### H2: Step-by-Step GPT-5 Fine-Tuning Process
*   Setting up your development environment

### H2: Advanced Fine-Tuning Techniques & Best Practices
*   Iterative fine-tuning and continuous learning

### H2: Deploying and Integrating Your Fine-Tuned GPT-5 Model
*   API integration for custom applications
"""
    
    print("\nGenerating article draft...")
    article_draft = generate_article_draft(example_brief_markdown)
    print(article_draft[:1000] + "\n...\n[Truncated for brevity. Full article would continue here.]") # Print first 1000 chars

import xmlrpc.client
import os

# Your WordPress site details
WORDPRESS_URL = "https://your-blog-domain.com/xmlrpc.php"
WORDPRESS_USERNAME = os.getenv('WORDPRESS_USERNAME')
WORDPRESS_PASSWORD = os.getenv('WORDPRESS_APP_PASSWORD') # Use an application password for security

def post_article_to_wordpress(title, content, categories=[], tags=[]):
    """
    Posts an article to WordPress using XML-RPC.
    """
    client = xmlrpc.client.ServerProxy(WORDPRESS_URL)
    
    # Define the post structure
    post = {
        'post_type': 'post',
        'post_status': 'publish',
        'post_title': title,
        'post_content': content,
        'post_category': [{'categoryId': category_id} for category_id in categories],
        'mt_keywords': ','.join(tags), # For compatibility with old metaWeblog API
        # 'wp_term_names': {'category': categories, 'post_tag': tags} # Newer way if supported by theme/plugin
    }
    
    try:
        # Get category IDs if you need to map names to IDs
        # categories_wp = client.wp.getTerms(0, WORDPRESS_USERNAME, WORDPRESS_PASSWORD, 'category')
        # print(categories_wp) # Debugging: inspect category IDs

        # Make sure you have the correct category IDs for your site.
        # For this example, let's assume we use names and map them or just use a default.
        # In a real setup, you'd fetch them or have a mapping.
        # For simplicity, we'll pass names as tags for this demo.
        
        # Publish the post using metaWeblog.newPost
        post_id = client.metaWeblog.newPost(0, WORDPRESS_USERNAME, WORDPRESS_PASSWORD, post, True)
        print(f"Article '{title}' posted successfully! Post ID: {post_id}")
        return post_id
    except xmlrpc.client.Fault as err:
        print(f"XML-RPC Error: {err.faultCode} - {err.faultString}")
        return None
    except Exception as e:
        print(f"An unexpected error occurred: {e}")
        return None

if __name__ == "__main__":
    # This would typically be the polished article after human review
    final_article_title = "The Future of AI: Hyper-Personalized Content with GPT-5"
    final_article_content = """
    # The Future of AI: Hyper-Personalized Content with GPT-5

    In 2025, content isn't just generic anymore. With GPT-5's advanced fine-tuning capabilities,
    bloggers are creating hyper-personalized experiences for their readers. This article dives
    into how AI is transforming content delivery, allowing for dynamic, user-specific narratives
    and tailored learning paths.

    ## Why Hyper-Personalization Matters
    Gone are the days of one-size-fits-all content. Modern readers demand relevance.
    GPT-5, when fine-tuned with user behavior data, can generate content that speaks directly
    to an individual's needs, interests, and even their current learning stage.

    ```python
    # Example of a personalized content generation snippet
    user_profile = {"interest": "AI automation", "experience_level": "intermediate"}
    personalized_prompt = f"Write a paragraph about advanced AI automation for {user_profile['experience_level']} users interested in {user_profile['interest']}."
    # response = client.chat.completions.create(...)
    ```

    ## Implementing Dynamic Content Workflows
    This requires a robust backend, leveraging tools like LangChain to orchestrate
    the content generation based on real-time user interactions and data analytics.
    ...
    [Full article content here]
    """
    
    # Replace with actual category IDs or names from your WordPress site
    # For demonstration, we'll use placeholder data.
    # In a real scenario, you'd fetch them: client.wp.getTerms(0, user, pass, 'category')
    article_categories = [] # e.g., [1, 5] if 1 is 'AI Blogging' and 5 is 'Automation'
    article_tags = ['AI', 'GPT-5', 'Personalization', '2025', 'Automation']
    
    # Note: Ensure XML-RPC is enabled on your WordPress site (Settings -> Writing -> Remote Publishing)
    # and you're using an Application Password (Users -> Your Profile -> Application Passwords).
    post_article_to_wordpress(final_article_title, final_article_content, article_categories, article_tags)