Author: salama-admin

  • 🧠How to Run LLMs Locally with Ollama and OpenAI Web UI

    🧠How to Run LLMs Locally with Ollama and OpenAI Web UI

    Artificial intelligence, especially large language models LLMs, has really taken off and is changing the way people across different industries work. These tools save so much time and make tasks a lot easier, which is why so many people are using them now.

    Due to the lack of permanent internet availability in many areas, such as Gaza City, where I live, due to the recent events, there is a difficulty in providing the internet permanently and continuously.

    To overcome this obstacle, I searched for ways on how to use LLM offline without internet, and I found many ideas and methods. In this article, I will help you by sharing my experience step by step.
    Let’s get started.


    🛠 Step 1: Install Ollama

    Ollama is a powerful runtime that simplifies the installation and execution of open-source LLMs on your local machine. It supports models like LLaMA 2, Mistral, and more.

    🔽 Installation

    For Windows / macOS / Linux, visit:
    👉 https://ollama.com/download


    🧠 Step 2: Choose and Install an LLM Model

    Ollama offers a growing catalog of pre-packaged models. Click here to view the list of the models, Click in any model and copy the command and paste it in the terminal

    Example:

    To download and start using Deepseek:

    ollama run deepseek-r1

    This will automatically download the model and launch an interactive prompt. You can also try other models like:


    💻 Step 3: Install Docker

    To enable a graphical interface (GUI) for interacting with your LLM, we’ll use an OpenAI-style Web UI powered by Docker.

    Installation:

    sudo apt-get update
    sudo apt-get install docker.io
    sudo systemctl start docker
    sudo systemctl enable docker

    🌐 Step 4: Install OpenAI Web UI with Docker

    Now let’s set up a friendly browser-based interface to interact with the local LLM, similar to ChatGPT.

    Run this command:

    docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main

    The web UI will be accessible at:
    🔗 http://localhost:3000

    It will automatically detect and connect to your running Ollama model.


    🚀 Step 5: Run and Enjoy!

    With both Ollama and Open WebUI running, you now have a powerful, private, and offline AI assistant.

  • Best 8 AI Plugins for WordPress (Free & Paid)

    Best 8 AI Plugins for WordPress (Free & Paid)

    Artificial Intelligence (AI) is no longer just a buzzword—it’s transforming how we build and manage websites. If you’re running a WordPress site, you’re in luck. There are dozens of AI-powered plugins that can help automate tasks, improve SEO, boost conversions, and create content like never before. Whether you’re a blogger, store owner, or developer, here are the best AI plugins for WordPress in 2024(free and premium) to level up your site.


    1. RankMath SEO (with AI Assistant)

    Best for: AI-powered SEO optimization
    Price: Free & Pro (starts at $69/year)

    Why it’s great:
    RankMath is one of the top SEO plugins, and now with AI integration, it can suggest keywords, optimize content in real-time, and even generate meta titles and descriptions using AI. It helps beginners and pros alike write SEO-friendly content faster.

    Key features:

    • Content AI assistant
    • Keyword suggestions and usage tips
    • Schema generation
    • Rank tracking (Pro)

    2. Bertha AI

    Best for: AI content writing for blogs and websites
    Price: Free & Pro (starts at $15/month)

    Why it’s great:
    Bertha AI integrates directly into the WordPress editor and can help you write anything—headlines, product descriptions, blog intros, or FAQs—on the spot.

    Key features:

    • AI blog and product description generator
    • Integration with page builders like Elementor
    • Multilingual support
    • Chat-style AI interface

    3. Jetpack AI Assistant

    Best for: Writing assistance and grammar corrections
    Price: Free trial, then from $8/month

    Why it’s great:
    Jetpack, from the team behind WordPress.com, now includes a powerful AI writing assistant. It’s ideal for bloggers and content creators looking for grammar checks, tone adjustments, and writing help inside the block editor.

    Key features:

    • AI content generation
    • Spelling and grammar improvements
    • Suggests headlines and outlines
    • Works in the native block editor

    4. AI Engine by Jordy Meow

    Best for: Building custom AI chatbots and AI content tools
    Price: Free & Pro (starts at $49/year)

    Why it’s great:
    This plugin lets you integrate OpenAI (ChatGPT) into WordPress, including adding a chatbot to your site, generating content, and even creating custom AI tools.

    Key features:

    • ChatGPT integration
    • AI content generation (bulk posts, excerpts)
    • Custom chatbot for visitors
    • GPT-4 support available

    5. Tidio AI (Lyro)

    Best for: AI customer support chatbot
    Price: Free & Premium plans (starts at $29/month)

    Why it’s great:
    Tidio combines live chat with Lyro—its conversational AI chatbot. It handles customer queries automatically, reducing support load and increasing conversions.

    Key features:

    • Conversational AI chatbot
    • Live chat integration
    • Pre-trained FAQ responses
    • CRM and email marketing features

    6. Quttera Web Malware Scanner (AI-based)

    Best for: Website malware detection and security
    Price: Free & Pro

    Why it’s great:
    This security plugin uses AI to detect malware, suspicious behavior, and vulnerabilities that traditional scanners might miss.

    Key features:

    • AI-based malware detection
    • Real-time threat alerts
    • Security log reports
    • External website scan option

    7. WordLift

    Best for: AI-powered content structuring and SEO
    Price: Paid (from €59/month)

    Why it’s great:
    WordLift is a semantic SEO plugin that uses AI to analyze your content and build knowledge graphs to improve your rankings and engagement.

    Key features:

    • Schema markup automation
    • Knowledge graph and entity linking
    • SEO automation for large content sites
    • Works with WooCommerce and multilingual sites

    8. Voicer or Play.ht

    Best for: AI voice generation for blogs and podcasts
    Price: Paid (varies)

    Why they’re great:
    These plugins turn your articles into realistic AI voiceovers for accessibility, engagement, or creating audio blogs.

    Key features:

    • Text-to-speech using AI voices
    • Audio player integration
    • Multilingual voice options
    • Podcast creation ready

  • Why You Should Read “Hands-On Machine Learning with Scikit-Learn and TensorFlow”?

    Why You Should Read “Hands-On Machine Learning with Scikit-Learn and TensorFlow”?

    Machine learning has evolved from a niche research field to one of the most in-demand skills in the tech world. Whether you’re looking to build intelligent systems for real-world use cases or just starting your AI journey, “Hands-On Machine Learning with Scikit-Learn and TensorFlow” offers one of the best practical roadmaps out there.

    Let’s break down the concepts, tools, and techniques covered in this hands-on approach to building smart, real-world machine learning solutions.


    🔍 What Is the Book About?

    This hands-on guide teaches you how to use two of the most popular Python machine learning libraries:

    • Scikit-Learn – Best for traditional ML models like regression, decision trees, and clustering.
    • TensorFlow/Keras – Ideal for deep learning: neural networks, computer vision, NLP, and more.

    The book strikes a balance between theory and practical implementation, showing how ML systems are built and deployed using real datasets.


    🧠 Core Concepts Covered

    1. Machine Learning Fundamentals

    • Types of ML: supervised, unsupervised, reinforcement
    • Overfitting, underfitting, generalization
    • Bias-variance tradeoff
    • Cross-validation and model evaluation

    2. Data Preparation and Pipelines

    • Handling missing values
    • Feature scaling (standardization, normalization)
    • Encoding categorical variables
    • Automating preprocessing with Scikit-Learn pipelines

    3. Classical Machine Learning with Scikit-Learn

    • Linear and logistic regression
    • Decision trees and random forests
    • Support Vector Machines (SVMs)
    • K-Nearest Neighbors (KNN)
    • Ensemble methods: voting, bagging, boosting

    🤖 Deep Learning with TensorFlow and Keras

    1. Neural Networks

    • Understanding neurons, layers, and activation functions
    • Feedforward and fully connected layers
    • Backpropagation and gradient descent

    2. Model Training Techniques

    • Learning rate scheduling
    • Batch normalization
    • Early stopping
    • Regularization (L1, L2, dropout)

    3. Advanced Architectures

    • Convolutional Neural Networks (CNNs) for image tasks
    • Recurrent Neural Networks (RNNs), LSTMs, and GRUs for time-series and sequences
    • Transformers for NLP (highlights in newer editions)

    🧰 Tools and Ecosystem

    • Jupyter Notebooks: interactive environment for writing and testing code
    • Matplotlib & Seaborn: data visualization
    • NumPy & Pandas: data manipulation and analysis
    • TensorBoard: visualizing training, model architecture, and performance
    • Keras: TensorFlow’s high-level API for easy model building

    📈 Projects and Real-World Examples

    The book is filled with end-to-end projects, such as:

    • Predicting housing prices
    • Classifying images (MNIST)
    • Training sentiment analysis models
    • Building a spam filter

    Each project emphasizes best practices like:

    • Data cleaning
    • Feature engineering
    • Model tuning
    • Saving/loading models
    • Deploying to production

    🚀 Why You Should Read It

    Whether you’re a data scientist, software engineer, or hobbyist, this book helps you:

    • Build intuition for how ML algorithms work
    • Apply ML techniques to real problems
    • Transition from traditional ML to deep learning
    • Use industry-standard tools to build scalable systems