Kishan ,
Ø Vishesh tells me he has received your invoice for My-Teacher development. He is arranging payment
Ø As far as My-Teacher balance work ( Mock Tests for Standards 9 to 12 ) is concerned , I have sent you a Whatsapp message this morning and await
your target date. Apart from that , there might be some work involved in connection with GRAPHIC improvement ( for which , one Mr Gaikwad will
visit my home tomorrow at 11 am ) . After this , for next 2 / 3 months , there will be no further development . After 3 months , I have planned a major
re-development of My-Teacher , about which , I had a long chat with ChatGPT , which I will send to you at proper time
Ø In this intervening period of next 2 months , I would like you to take up development of BLOG GENIE V 2.0 ( Perpetual Machine ) as per diagram
Ø attached + today’s chat with ChatGPT ( reproduced below )
Ø After you had time to study , let me know your convenience when you can visit my home for a face-to-face meeting when Vishesh could remain present ( week-ends )
Ø When you visit , do bring along your approx. estimate of man-hours needed / cost / target date
Hcp
27 Dec 2024
To provide improved instructions for Kishan, I will consolidate the details from the diagram and notes you shared earlier, while also integrating recent advancements in LLM (Large Language Models) and SLM (Small Language Models). Here’s a revised and comprehensive system architecture proposal tailored to your vision of a "Perpetual AI Machine":
Proposed System Architecture: Perpetual AI Machine
Core Objectives
1. Automate content creation, curation, and publishing with minimal human intervention.
2. Enable seamless integration of new ideas (Memory Blocks) and ensure continuous learning and updating.
3. Facilitate multi-platform publishing (Personal.ai, Blogger, LinkedIn, etc.).
4. Support dynamic generation of new topics and questions relevant to evolving trends.
Key Modules and Workflow
1. Data Sources and Input Modules
o Primary Portals:
o
§ Virtual Avatar (hemenparekh.ai): Captures ideas, blogs, and public thoughts.
§
§ My-Teacher.in: Focuses on education, especially topics beyond the syllabus.
§
§ Surprise-Exam.in: Generates exam-related content and questions.
o
o External Feeds:
o
§ APIs from reputable news organizations (e.g., The Economic Times, Times of India).
§ Topic-specific scrapers or spider crawlers to fetch fresh and relevant content.
§
2. Memory Management System
o Memory Blocks:
o
§ Dynamic storage of user-created ideas, topics, and generated content.
§ Categorized by relevance, novelty, and impact.
§
o New Memory Blocks Integration:
o
§ Automatically analyze incoming data to identify patterns and trends.
§ Generate new topics and questions using NLP and machine learning models.
§
3. Big Rock Database
o Centralized repository for all generated content and blogs.
o Designed to support:
§ Fast retrieval and indexing.
§ Version control and historical comparison of content.
§
4. Content Generation and Curation
o Blog Genie:
o
§ Enhanced with GPT-based models for automated blog generation.
§ Capable of analyzing news articles, extracting key insights, and framing blogs tailored to each platform's audience.
§
o Topic Generator:
o
§ AI-powered suggestion engine for new blog ideas and exam questions.
§ Incorporates semantic analysis to maintain relevance and quality.
§
§
5. Publishing and Distribution
o Platforms:
o
§ Personal.ai, Blogger, LinkedIn (direct API-based integration for posting).
§
o Automated Email Module:
o
§ Targets policymakers and stakeholders with relevant blogs and findings.
§
o Feedback Loop:
o
§ Monitors user engagement and platform-specific metrics to refine content.
Technology Stack
1. LLM/SLM Integration
o LLMs (e.g., OpenAI GPT models):
o
§ Power the Blog Genie and Topic Generator modules.
§ Perform advanced text analysis and synthesis.
§
o SLMs:
o
§ Handle lightweight tasks like filtering news feeds or suggesting quick updates.
2. Backend and Data Handling
o Database:
o
§ PostgreSQL or MongoDB for structured and unstructured data.
§
o Data Pipelines:
o
§ Apache Kafka for real-time data streaming and processing.
§
o Storage:
o
§ AWS S3 for scalable storage of content archives.
3. Frontend Framework
o User Portals:
§ React.js for dynamic and responsive interfaces.
§ Integrated dashboards for monitoring and controlling the system.
4. Automation and APIs
o Web Scraping:
§ BeautifulSoup/Scrapy for extracting data from news websites.
§
o Publishing APIs:
o
§ LinkedIn API, Blogger API, and others for direct posting.
o
5. Deployment
o Cloud Hosting:
o
§ AWS, Google Cloud, or Azure for scalable and reliable hosting.
§
o Containerization:
o
§ Docker and Kubernetes for modular and fault-tolerant deployment.
Advanced Features
1. Knowledge Graph
o Visualizes relationships between Memory Blocks, blogs, and generated topics.
o Helps identify content gaps and emerging areas of interest.
o
2. Personalization Engine
o
o Customizes content based on audience preferences and past engagement metrics.
o
3. Continuous Learning
o Integrates Reinforcement Learning to refine content based on feedback.
o Automatically incorporates new developments in AI and NLP.
o
4. Security and Compliance
o Ensures compliance with GDPR and other regulations.
o Secures sensitive data through encryption and secure APIs.
Instructions for Development
1. Set Up Data Pipelines:
o Implement robust ETL pipelines to feed data into the system.
o
2. Train and Fine-Tune Models:
o Use pre-trained LLMs and adapt them to domain-specific requirements.
o
3. Build Modular APIs:
o Create RESTful APIs for seamless integration across platforms.
o
4. Test and Iterate:
o Deploy in phases, starting with limited functionality to gather feedback.
o
5. Optimize for Scale:
o Design with scalability in mind to accommodate growing user bases and content demands.
Would you like me to include example code snippets or provide further details on specific modules? Let me know!
4o
You said:
Hey , thanks , please do provide example code snippets
ChatGPT said:
ChatGPT
Here are some example code snippets for critical components of your Perpetual AI Machine architecture. These snippets are simplified and serve as starting points for your developer, Kishan, to build and customize the system.
1. Web Scraping and Data Collection
Purpose: Fetch news articles or other relevant data from external sources.
Tools: Python with BeautifulSoup/Scrapy.
python
Copy code
import requests
from bs4 import BeautifulSoup
def scrape_news(url):
headers = {'User-Agent': 'Mozilla/5.0'}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.
# Extract headline and summary
articles = []
for item in soup.find_all('div', class_='news-card'):
title = item.find('h2').text
summary = item.find('p').text
link = item.find('a')['href']
articles.append({'title': title, 'summary': summary, 'link': link})
return articles
# Example usage
url = "https://example-news-site.
news_data = scrape_news(url)
print(news_data)
2. Content Generation with GPT-Based Models
Purpose: Generate blogs and articles automatically.
Tools: OpenAI API or similar LLMs.
python
Copy code
import openai
openai.api_key = "your-openai-api-key"
def generate_blog(topic):
prompt = f"Write a detailed blog post about {topic}. Include key insights, examples, and a call to action."
response = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
max_tokens=800,
temperature=0.7
)
return response['choices'][0]['text']
# Example usage
topic = "Importance of AI in Modern Education"
blog_content = generate_blog(topic)
print(blog_content)
3. Data Storage and Memory Blocks
Purpose: Store and retrieve Memory Blocks for reuse and updating.
Tools: MongoDB for dynamic data structures.
python
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from pymongo import MongoClient
client = MongoClient("mongodb://
db = client["perpetual_ai"]
memory_blocks = db["memory_blocks"]
def save_memory_block(block):
memory_blocks.insert_one(
def fetch_memory_blocks(query):
return list(memory_blocks.find(query)
# Example usage
new_block = {
"topic": "AI in Healthcare",
"content": "AI is transforming healthcare with applications in diagnosis, treatment, and patient monitoring.",
"date": "2024-12-27"
}
save_memory_block(new_block)
# Fetch all memory blocks
blocks = fetch_memory_blocks({})
print(blocks)
4. Automated Blog Publishing
Purpose: Publish content to platforms like LinkedIn or Blogger.
Tools: LinkedIn API.
python
Copy code
import requests
def publish_to_linkedin(content, access_token):
api_url = "https://api.linkedin.com/v2/
headers = {
'Authorization': f'Bearer {access_token}',
'Content-Type': 'application/json'
}
data = {
"content": {
"contentEntities": [{
"entityLocation": "https://example.com/blog-post
"thumbnails": [{
"resolvedUrl": "https://example.com/image.jpg
}]
}],
"title": "Blog Post Title"
},
"text": {
"text": content
},
"distribution": {
"linkedInDistributionTarget": {}
},
"owner": "urn:li:person:your-profile-
}
response = requests.post(api_url, json=data, headers=headers)
return response.status_code
# Example usage
blog_content = "AI is revolutionizing education. Read more on our portal!"
access_token = "your-linkedin-access-token"
status = publish_to_linkedin(blog_
print(f"Publish status: {status}")
5. Generating New Topics from Memory Blocks
Purpose: Dynamically suggest new blog topics based on stored data.
Tools: Python with scikit-learn for topic modeling.
python
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from sklearn.feature_extraction.
from sklearn.decomposition import NMF
def suggest_topics(memory_blocks):
documents = [block['content'] for block in memory_blocks]
vectorizer = TfidfVectorizer(max_features=
X = vectorizer.fit_transform(
nmf = NMF(n_components=5, random_state=42)
W = nmf.fit_transform(X)
H = nmf.components_
words = vectorizer.get_feature_names_
topics = []
for i, topic in enumerate(H):
topic_words = [words[i] for i in topic.argsort()[-5:]]
topics.append(" ".join(topic_words))
return topics
# Example usage
blocks = fetch_memory_blocks({})
new_topics = suggest_topics(blocks)
print("Suggested Topics:", new_topics)
6. Feedback Loop for Continuous Learning
Purpose: Refine and adapt content based on user engagement.
Tools: Python with Matplotlib for visualization.
python
Copy code
import matplotlib.pyplot as plt
def plot_engagement(data):
dates = [item['date'] for item in data]
views = [item['views'] for item in data]
likes = [item['likes'] for item in data]
plt.figure(figsize=(10, 5))
plt.plot(dates, views, label='Views')
plt.plot(dates, likes, label='Likes')
plt.xlabel('Date')
plt.ylabel('Count')
plt.title('User Engagement Over Time')
plt.legend()
plt.show()
# Example usage
engagement_data = [
{"date": "2024-12-20", "views": 120, "likes": 15},
{"date": "2024-12-21", "views": 150, "likes": 20},
{"date": "2024-12-22", "views": 180, "likes": 25},
]
plot_engagement(engagement_
Let me know if you need more specific code examples for any part of the system or want me to focus on a particular module!
From: Hemen Parekh [mailto:hcp@recruitguru.com]
Sent: 16 August 2024 14:07
To: 'Manish Chauhan'; ''System Administrator'; 'officeassistant@
Cc: 'vishesh@incomegroup.in'; 'nirmit@3pConsultants.co.in'; ''Mitchelle'; 'Abhijeet Swarnkar'; 'prashant@3pConsultants.co.in'
Subject: FW: I WANT TO TAKE IT EASY
Dear All ,
www.HemenParekh.ai , is all “ General Knowledge “ , when you look at those 81339 topics AND 51564 QUESTIONS
If our Perpetual AI Machine , is configured to find / access , news of current political / social / administrative / economic , TOPICS and generate new blogs around these topics ( using Blog Genie ) , then the GENERAL KNOWLEDGE of www.HemenParekh.ai , keeps increasing exponentially
That will generate ( in Personal.ai ) , thousands of new Memory Blocks
This will be my “ Reformer / Crusader Persona “ , in Personal.ai
I am confident that , given this huge database ( of ever-increasing TOPICS and QUESTIONS ) , Kishan should be able to add ( target not decided as yet ) > Mock Tests for IAS and Mock Tests for IPS , as shown in attachment
hcp
From: Hemen Parekh [mailto:hcp@recruitguru.com]
Sent: 16 August 2024 11:55
To: 'Manish Chauhan'; 'System Administrator (systemadmin@3pconsultants.co.
Cc: 'vishesh@incomegroup.in'; 'nirmit@3pConsultants.co.in'; 'Mitchelle (mitchelle@3pconsultants.co.in
Subject: I WANT TO TAKE IT EASY
Manish – Sandeep – Sanjivani – Abhijit – Prashant - Kishan ,
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