This guide provides essential information about property rights and documentation in Bangalore, Karnataka. It covers key aspects of property ownership, legal documentation, and the transfer process that every property buyer or owner should understand. Whether you're purchasing an apartment, land, or any real estate in Bangalore, these concepts will help you navigate the legal landscape of property ownership.

Schedules in Karnataka Property Documents​

In Bangalore (Karnataka) property registration documents, schedules are standardized sections that describe the property in detail:

Schedule A​

• Describes the entire property development or project
• Includes location details (Doddabanahalli Village, Bidarahalli Hobli, etc.)
• Contains survey numbers, boundary descriptions, and total area
• Establishes the legal identity of the whole development

Schedule B​

• Describes the undivided share of land associated with a specific unit
• Typically measured in square meters/square feet
• Represents proportional ownership in common areas and land
• Essential for apartment complexes where land is shared among owners

Schedule C​

• Details the specific property unit being transacted
• Includes apartment number, floor, exact measurements
• Describes physical boundaries of the unit
• Specifies exclusive areas owned by the buyer

cURL is a command-line tool used to interact with APIs and perform HTTP requests. The name cURL is derived from "Client for URLs" or simply "command URL". Below is a guide on how to use cURL for common HTTP methods.

Setting Up a Local SQL Connection​

Step 1: Install a Database Server​

MySQL/MariaDB​

# Ubuntu/Debian
sudo apt install mysql-server

# macOS (using Homebrew)
brew install mysql

# Windows
# Download installer from mysql.com

Groq offers incredibly fast LLM inference through its API service, including access to Llama models. Here's a complete guide to setting up and using Groq with Llama models at no cost.

What is Groq?​

Groq is an AI inference engine that provides remarkably fast LLM responses using specialized hardware accelerators (LPUs - Language Processing Units). Their free tier allows access to several models, including Meta's Llama family.

Chatbots & Assistants​

1. Claude.ai

   • Anthropic's conversational AI with a 100K context window on the free tier.
   • Great for long-form conversations and document analysis.

2. Microsoft Copilot

   • Free access to GPT-4 with image generation and web browsing capabilities.
   • Integrated into Microsoft Office and Edge.

3. Bard (Google Gemini)

   • Google's AI assistant with multimodal capabilities.
   • Free for general use with access to real-time web search.

4. HuggingChat

   • Open-source chatbot using various open models.
   • Free and community-driven.

5. Poe

   • Platform offering free limited access to multiple AI models like GPT-4, Claude, and others.

Ollama is an open-source tool that is designed to simplify the process of running large language models locally, meaning on your own hardware. The idea here is very simple. As you know, if you want to run large language models or use a model, most likely you will have to rely on paid services like OpenAI, ChatGPT, and others.

With Ollama, you don't have to pay for anything—it's free, and that's the beauty. Ollama sits at the center and allows developers to pick different large language models depending on the situation and their needs. At its core, Ollama uses a command-line interface (CLI) to manage backend tasks like installation and execution of different models, all of which run locally. Ollama abstracts away the technical complexities involved in setting up these models, making advanced language processing accessible to a broader audience, including developers, researchers, and hobbyists. In a nutshell, Ollama provides a straightforward way to download, run, and interact with various models or LLMs without relying on cloud-based services or dealing with complex setup procedures.

What is an Open LLM?​

An Open LLM (Large Language Model) is a language model whose architecture, weights, and often training code are openly available for anyone to use, modify, and run. Unlike proprietary LLMs (like OpenAI’s GPT-4 or Google Gemini), open LLMs can be run locally, fine-tuned, and integrated into custom workflows without vendor lock-in or sending your data to third-party servers.

The ideal way to clone a GitHub repository to your local folder is to use a PAT (Personal Access Token).
A PAT provides secure, token-based authentication instead of your GitHub password.
It is especially required for accessing private repositories or when two-factor authentication is enabled.

Horizontal scaling and vertical scaling are two strategies for increasing the capacity and performance of your application or infrastructure:

Horizontal Scaling (Scaling Out)​

• Definition: Adding more machines or instances to your system.
• How: You deploy additional servers (e.g., more VMs, containers, or physical machines) to distribute the load.
• Benefits:
  • Increases fault tolerance and availability.
  • Easier to scale dynamically (especially in cloud environments).
  • No single point of failure.
• Example: Adding more web servers behind a load balancer.

Here’s a detailed step-by-step guide to develop an AngularJS application, package it as a .war file, and deploy it with a Java backend.

1. Project Structure​

Organize your project like this:

your-project/
├── src/
│   └── main/
│       ├── java/           # Java backend code (e.g., Spring controllers)
│       └── webapp/         # Web resources (AngularJS app, HTML, CSS, JS)
│           ├── index.html
│           └── scripts/
│               └── angular/
│                   └── spotfireSpa/
│                       └── controller.js
├── pom.xml                 # Maven configuration