rsrini7

You are an expert technical writer and an automated documentation maintenance system. Your primary goal is to ensure the project has a complete, accurate, and up-to-date set of documentation in a `/docs` folder.

You will operate in two phases:
**Phase 1: Situational Analysis & Planning**
**Phase 2: Markdown Generation**

### PHASE 1: SITUATIONAL ANALYSIS & PLANNING

First, perform a detailed analysis of the current state of the project.

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* **Role and Purpose**: The AI is designated as a "prompt coach" with the mission to create a prompt blueprint that transforms the assistant into a personal AI tutor. This tutor will methodically quiz the user to diagnose their current AI level and deliver progressively harder lessons to stretch their understanding.
* **Framework**: The prompt follows a four-section blueprint:
    * **Purpose** (Goal, Meta-switches, Mode & Effort)
    * **Instructions** (Behavior & Rules)
    * **Reference** (Context, Data, Materials)
    * **Output** (Expected Format & Length)
* **Workflow Rules**:
    * **Section-by-section**: No skipping ahead; the AI handles one section at a time.
    * **Full question set**: For the current section, the AI shows every question and provides a concrete example answer for each.
    * **Gatekeeping**: The AI waits until all questions are answered. If an answer is unclear, it asks a follow-up question.

rsrini7

graph TD
    subgraph "Admin Interface"
        AdminUI[Admin React UI]
    end

    subgraph "Broadcast Microservice (Spring Boot WebFlux on K8s)"
        AdminAPI["/api/admin/broadcast (REST)"]
        SSEEndpoint["/api/notifications/sse (SSE)"]
        BroadcastService[Broadcast Service Logic]
        KafkaProducer[Kafka Producer]

rsrini7

No BS Mathematics for AI 
Bootstrapping Blueprint
Copy & Paste Framework
Power Paper Playbook
Portfolio Precision
PyTorch Protocol
Elite Engineer Essentials
Cutting-Edge Capsule
Six-Figure Secrets
Salary Seduction

rsrini7

#Requires -Version 5.1
<#
.SYNOPSIS
    Converts a directory of Markdown files into a styled HTML documentation site with a navigation menu.

.DESCRIPTION
    This script automates the process of generating a simple, clean HTML documentation website from a collection of Markdown (.md) files.
    It uses Pandoc to perform the conversion and supports Mermaid.js for diagrams.

    Features:

rsrini7

How multimodal RAG extends traditional RAG to include various media types like images, video, and audio.

Key topics covered include:

An explanation of how multimodal RAG works by retrieving information from diverse sources such as audio, images, and text, and then using this information to generate responses.

Discussions on different approaches to multimodal RAG:

  Joint Embedding: Using a single model to encode different data types into a shared vector space.
  Grounded Modality: Converting all data types into a single modality, typically text, before encoding.

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Key Insights from DeepSeek's Advancements and its innovative approach to developing powerful AI models, even with resource constraints. Their new paper, focusing on the architecture and training of next-generation models, and the practical implications of their DeepSeek V3 model.

Core Points from the First Analysis (Focus on New Paper and R2 Model):

New DeepSeek Paper: A paper dated May 14, 2025, outlines the architecture and training methods for DeepSeek's upcoming models, with a particular focus on the anticipated R2 model.

Cost-Effective Training: DeepSeek emphasizes that smart software-hardware co-design enables the cost-efficient training of large models, making it feasible for smaller teams to compete.

Multi-head Latent Attention (MLA): This technique is employed within the transformer architecture to compress information into a smaller vector space, leading to faster computations and reduced memory use.

rsrini7

I need you to analyze this codebase as an expert software architect. Please provide:

### Project Overview
- Core purpose and value proposition of this application
- Problem domain it addresses and target users/stakeholders
- Business context and where it fits in a larger ecosystem

### Technical Architecture
- Key architectural patterns and design principles employed
- System components and their interactions (with emphasis on interfaces)

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**Apache Kafka 4.0 Details:**

* **Cloud-Native Focus:** The primary theme of Kafka 4.0 is its continued evolution towards being more cloud-native (from video).
   
* **ZooKeeper Removal (KIP-500):**
    * ZooKeeper has been fully removed and deprecated (from video)
    * KRaft is now the standard for metadata management, offering improved scalability and resilience (from video).
    * This change simplifies cloud deployments by eliminating a component that posed networking challenges (from video).
   
* **KIP-848: New Consumer Rebalance Protocol**

rsrini7

# -*- coding: utf-8 -*-
"""
N8N-Inspired AI Automation Workflow Builder using Streamlit and LangGraph
"""
# Subscribe to the Deep Charts YouTube Channel (https://www.youtube.com/@DeepCharts)

import streamlit as st
import uuid
import os
import re