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docs/tech_docs/linux/Command-Line-Mastery-for-Web-Developers.md

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+# Command Line Mastery for Web Developers
+
+## Introduction to Command Line for Web Development
+- **Why Command Line**: Importance in modern web development.
+- **Getting Started**: Basic CLI commands, navigation, file manipulation.
+
+## Advanced Git Techniques
+- **Rebasing and Merging**: Strategies for clean history and resolving conflicts.
+- **Bisect and Reflog**: Tools for debugging and history traversal.
+- **Hooks and Automation**: Customizing Git workflow.
+
+## NPM Mastery
+- **Scripting and Automation**: Writing efficient NPM scripts.
+- **Dependency Management**: Handling version conflicts, updating packages.
+- **NPM vs Yarn**: Comparing package managers.
+
+## Automating with Gulp
+- **Setting Up Gulp**: Basic setup and configuration.
+- **Common Tasks**: Examples like minification, concatenation, and image optimization.
+- **Optimizing Build Process**: Streamlining tasks for efficiency.
+
+## Bash Scripting Essentials
+- **Script Basics**: Writing and executing scripts.
+- **Useful Commands**: Loops, conditionals, and input handling.
+- **Real-World Scripts**: Practical examples for automation.
+
+## SSH for Secure Remote Development
+- **Key Management**: Creating and using SSH keys.
+- **Remote Commands**: Executing commands on remote servers.
+- **Tunneling and Port Forwarding**: Secure access to remote resources.
+
+## Command Line Debugging Techniques
+- **Basic Tools**: Introduction to tools like `curl`, `netstat`, `top`.
+- **Web-Specific Debugging**: Analyzing network requests, performance issues.
+- **Logs Analysis**: Working with access and error logs.
+
+## Docker Command Line Usage
+- **Docker CLI Basics**: Common commands and workflows.
+- **Dockerfiles**: Creating and understanding Dockerfiles.
+- **Container Management**: Running, stopping, and managing containers.
+
+## Command Line Version Control
+- **Version Control Systems**: Git, SVN command line usage.
+- **Branching and Tagging**: Best practices for branch management.
+- **Stashing and Cleaning**: Managing uncommitted changes.
+
+## Performance Monitoring via CLI
+- **Tools Overview**: `htop`, `vmstat`, `iostat`.
+- **Real-Time Monitoring**: Tracking system and application performance.
+- **Bottleneck Identification**: Finding and resolving performance issues.
+
+## Securing Web Projects through CLI
+- **File Permissions**: Setting and understanding file permissions.
+- **SSL Certificates**: Managing SSL/TLS for web security.
+- **Security Audits**: Basic command line tools for security checking.
+
+## Text Manipulation and Log Analysis
+- **Essential Commands**: Mastery of `sed`, `awk`, `grep`.
+- **Regular Expressions**: Using regex for text manipulation.
+- **Log File Parsing**: Techniques for efficient log analysis.
+
+## Interactive Examples and Challenges
+- **Practical Exercises**: Step-by-step challenges for each section.
+- **Solution Discussion**: Explaining solutions and alternatives.
+
+## Resource Hub
+- **Further Reading**: Links to advanced tutorials, books, and online resources.
+- **Tool Documentation**: Official documentation for the mentioned tools.
+
+## FAQ and Troubleshooting Guide
+- **Common Issues**: Solutions to frequent problems and errors.
+- **Tips and Tricks**: Enhancing usability and productivity.
+
+## Glossary
+- **Key Terms Defined**: Clear definitions of CLI and development terms.
+

docs/tech_docs/mkdocs.md

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+# Setting Up a Virtual Environment for KnowledgeBase-Tech MKDocs Site
+
+## Update PATH Environment Variable
+
+Before installing `virtualenv`, ensure your PATH includes the directory where Python packages are installed. If you see a message like "The script virtualenv is installed in '/home/medusa/.local/bin' which is not on PATH," you'll need to update your PATH. Add the following line to your `.bashrc` or `.profile` file:
+
+```bash
+export PATH="$HOME/.local/bin:$PATH"
+```
+
+Then, reload your profile:
+
+```bash
+source ~/.bashrc
+```
+
+or for a login shell:
+
+```bash
+source ~/.profile
+```
+
+## Install Virtual Environment
+
+Ensure Python and pip are installed, then install `virtualenv`:
+
+```bash
+pip3 install virtualenv
+```
+
+## Create the Virtual Environment
+
+Create a virtual environment named `KnowledgeBase-Tech_env`:
+
+```bash
+virtualenv KnowledgeBase-Tech_env
+```
+
+## Activate the Virtual Environment
+
+To start using the virtual environment:
+
+```bash
+source KnowledgeBase-Tech_env/bin/activate
+```
+
+## Install Dependencies
+
+While the environment is active, install any required packages:
+
+```bash
+pip install <package-name>
+```
+
+## Freeze Requirements
+
+To keep track of your project's dependencies, you can freeze the installed packages into a `requirements.txt` file:
+
+```bash
+pip freeze > requirements.txt
+```
+
+This file can be used to install the exact versions of the required packages on other setups or by other developers working on the project.
+
+## Deactivate the Virtual Environment
+
+When you're done working, deactivate the environment:
+
+```bash
+deactivate
+```
+
+## Working with the Environment
+
+Remember to activate the `KnowledgeBase-Tech_env` environment every time you work on the KnowledgeBase-Tech Hugo site. This ensures all dependencies are isolated to this specific project.

docs/tech_docs/python/JupyterLab.md

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+# Installing JupyterLab on a Linux Server
+
+This guide outlines the steps to install JupyterLab on a Linux server, enabling powerful data analysis and machine learning capabilities with remote access.
+
+## Prerequisites
+
+- A Linux server (Debian/Ubuntu or RHEL-based)
+- SSH access to the server
+- Basic command-line proficiency
+
+## Step 1: Connect to Your Server
+
+Start by establishing an SSH connection to your server.
+
+```bash
+ssh username@your_server_ip
+```
+
+Replace `username` with your actual server username and `your_server_ip` with the server's IP address.
+
+## Step 2: Update Your Server
+
+Ensure your server's package lists and installed packages are updated.
+
+### For Debian/Ubuntu:
+
+```bash
+sudo apt-get update && sudo apt-get upgrade
+```
+
+### For RHEL-based systems:
+
+```bash
+sudo yum update
+```
+
+## Step 3: Install Python and Virtual Environment
+
+JupyterLab requires Python. Install Python 3 and the package to manage virtual environments.
+
+### For Debian/Ubuntu:
+
+```bash
+sudo apt-get install python3-venv python3-pip
+```
+
+### For RHEL-based systems:
+
+Ensure you have access to the EPEL repository, then:
+
+```bash
+sudo yum install python3-pip python3-virtualenv
+```
+
+## Step 4: Create and Activate a Virtual Environment
+
+Creating a virtual environment isolates your JupyterLab setup.
+
+```bash
+python3 -m venv jupyterlab_env
+source jupyterlab_env/bin/activate
+```
+
+## Step 5: Install JupyterLab
+
+With the virtual environment activated, install JupyterLab using pip.
+
+```bash
+pip install jupyterlab
+```
+
+## Step 6: Start JupyterLab
+
+Run JupyterLab, configuring it to allow remote access and to prevent it from trying to open a browser automatically.
+
+```bash
+jupyter lab --ip=0.0.0.0 --no-browser
+```
+
+**Note**: `--ip=0.0.0.0` makes JupyterLab accessible on all network interfaces of your server. For security, consider setting up a more restrictive IP or using additional security measures like SSH tunneling or a VPN.
+
+## Step 7: Access JupyterLab
+
+Upon starting JupyterLab, the terminal will display a URL beginning with `http://127.0.0.1:8888`. Replace `127.0.0.1` with your server's IP address or hostname to access JupyterLab from your browser.
+
+## Step 8: Secure Your JupyterLab Instance
+
+It's crucial to secure your JupyterLab instance, especially if accessible over the public internet.
+
+### Set a Password for JupyterLab
+
+Run this command and follow the prompts to create a password:
+
+```bash
+jupyter notebook password
+```
+
+### Consider Additional Security Measures
+
+- Use SSH tunneling for a secure connection.
+- Configure a reverse proxy with SSL encryption.
+- Employ firewall rules to restrict access.
+
+## Conclusion
+
+You've now set up JupyterLab on your Linux server, ready for data analysis and machine learning projects with the power of server-grade hardware.
+
+---