Cursor: Coding with AI


Cursor: Coding with AI
.MP4, AVC, 1920×1080, 30 fps | English, AAC, 2 Ch | 2h 45m | 434 MB
Instructor: Elvis Saravia​

Learn how to code and build AI-powered web applications with Cursor.

What you will learn

  • Learn how to use the most important AI-powered features in Cursor
  • Best practices and tips for how to best leverage Cursor capabilities like Chat and Composer
  • Build web applications using Cursor Composer Agents

    OVERVIEW

    This course focuses on teaching how to code with AI and build AI-powered applications with Cursor.

    After completing the course, students will have a solid understanding of how to use Cursor and how to build web applications with it.

    PREREQUISITES

    All the code examples are built around Python so a basic understanding of Python is required.

    The main tool you will use is Cursor. More details and instructions about how to install Cursor are provided in the course.

    We also recommend you get a Pro account for Cursor. You could get started with the 14-day trial but keep in mind that there might be differences in functionalities and behaviors.

    You will also need an OpenAI paid account to access your own API Keys. This will be needed for the final project.

    TOPICS

    Key concepts covered in the course include:

    • Cursor Introduction: An introduction to Cursor, including installation steps, settings, pricing, and how to set up your environment.
    • Cursor Tab: Covers all the autocomplete capabilities of Cursor and scenarios where to use or combine features like Cursor Prediction and Multi-Edits.
    • Cursor Chat: Students will learn best practices for how to explore codebases with Chat and different approaches for how to optimize context to get the best results from the AI and improve your coding workflow.
    • Cmd K: Leverage AI coding capabilities right from the editor and terminal.
    • Cursor Composer: Provides a comprehensive overview of Composer agents and how to build web applications.

    More Info

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  • Crypto and Blockchain for Beginners The Ultimate Guide

    Crypto and Blockchain for Beginners The Ultimate Guide

    File INFO:
    Name: 1 Rapid Fire Q&A
    Format:mp4
    Size: 3.69 GB
    Duration:00:11:08 Click to expand…

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    – If you enjoy my posts, feel free to leave a "Thanks" in the comments.

    Critical NetWork Services DNS, DHCP, and NAT


    Released 3/2026 By Andrew Mallett MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 25m 58s | Size: 203 MB​

    Many entry-level administrators struggle to understand how essential network services work together to keep systems connected and secure.

    What you’ll learn
    Many entry-level administrators struggle to understand how essential network services work together to keep systems connected and secure. In this course, Critical Network Services DNS, DHCP, and NAT, you’ll gain the ability to configure, manage, and troubleshoot core network services with confidence. First, you’ll explore how DNS enables name resolution across networks. Next, you’ll discover how DHCP automates IP address assignment and network configuration. Finally, you’ll learn how to implement and manage NAT to allow secure communication between internal and external networks. When you’re finished with this course, you’ll have the skills and knowledge of critical network services needed to support and maintain small to medium-sized networks in an entry-level administrator role.

    Welcome to Check it Every Days
    If you have any troubles with downloading, PM me

    Happy Learning!!


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    Creating Personal AI Agents (2026)


    Released 03/2026 By Kamran Ayub MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 51m 8s | Size: 219 MB​

    How can AI agents help you save time and boost productivity without wasting effort? In this course, Creating Personal AI Agents, you’ll learn to develop and apply custom AI agents for professional use.

    What you’ll learn
    How can AI agents help you save time and boost productivity without wasting effort? In this course, Creating Personal AI Agents, you’ll learn to develop and apply custom AI agents for professional use. First, you’ll explore how to build simple AI agents using foundational concepts like ReAct. Next, you’ll discover how to customize agents for specific tasks using no-code and low-code tools. Finally, you’ll learn how to integrate AI agents responsibly by following ethical considerations and best practices. When you’re finished with this course, you’ll have the skills and knowledge needed to design effective AI agents, streamline your workflows, and enhance your productivity.

    Welcome to Check it Every Days
    If you have any troubles with downloading, PM me

    Happy Learning!!


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    Create Agents with v0 and Vercel (No Code Required)


    Create Agents with v0 and Vercel (No Code Required)
    .MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 20m | 56.5 MB
    Instructor: Eve Porcello​

    This course shows how to build a real, working AI agent using v0, the natural‑language app builder from Vercel-no coding required. By describing what you want in your own words, you can create and evolve an agent that reflects how modern teams actually work with AI.

    Designed for product managers, designers, marketers, and other nontraditional developers, the course goes beyond demos and isolated prompts. You complete an end‑to‑end, production‑shaped project: building an agent, training it with context using file uploads, storing and managing customer feedback, iterating features through follow‑up prompts, and deploying a live app to Vercel.

    By the end, you’ll have hands‑on experience creating, refining, and shipping an AI‑powered product for real business use cases.

    Learning objectives

    • Create and iterate on an AI agent using natural‑language prompts.
    • Enable the agent to take meaningful actions on user input.
    • Store, seed, and manage customer feedback with a database.
    • Add features and modify layouts through prompt‑driven updates.
    • Deploy a live, production‑ready agent to Vercel.

    Homepage

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    Create A Victorian Room With Blender And Substance Painter


    Create A Victorian Room With Blender And Substance Painter
    Published 7/2022
    MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 13.92 GB | Duration: 18h 33m​

    Learn Blender while creating a detailed environment suitable for animation or games.

    What you’ll learn
    Model 3D objects in Blender
    Texture 3D models in Substance Painter
    Render an animation using Blender’s Cycles render engine
    Edit a video with Blender’s Video Sequence Editor
    Requirements
    Blender and Substance Painter are used in this course. The free paint program, Krita, is used briefly to edit images.
    Description
    In this course you’ll learn how to create a detailed interior scene using tools and techniques that apply equally to both animation and video games. We’ll use Blender to create the 3D models of the furniture, accessories, and the structure of the room. And then we’ll work on UV mapping all of the 3D objects, getting them ready for export, and taking them into Substance Painter for texturing. You’ll learn how to use layer masks in Substance Painter to quickly create realistic textures for your 3D objects.We’ll set up a Blender export template in Substance Painter, and export our texture maps so that we can use Blender’s Node Wrangler to easily apply the textures to our scene. Next we’ll work on lighting the scene, and rendering using Blender’s Cycles render engine. We’ll animate the camera and use Blender’s Video Sequence Editor to edit our rendered shots together. We’ll even add some music and then render it all out to a single mp4 video file.If you’re interested in creating environments for animation or video games, then this course is for you. You’ll learn how to create a realistic, detailed scene from the first polygon to the final render. So join me as we "Create a Victorian Room with Blender and Substance Painter."

    Overview

    Section 1: Introduction

    Lecture 1 Introduction to the Course

    Lecture 2 Setting Up a Reference Figure

    Section 2: Modeling the Furniture

    Lecture 3 Beginning the Coffee Table

    Lecture 4 Continuing the Coffee Table

    Lecture 5 Finishing the Coffee Table

    Lecture 6 Modeling the Round End Table

    Lecture 7 Completing the Round End Table

    Lecture 8 Starting the Arm Chair

    Lecture 9 Refining the Shape of the Arm Chair

    Lecture 10 Creating the Legs of the Chair

    Lecture 11 Using the Path Tool

    Lecture 12 Creating the Front Panels of the Arm Chair

    Lecture 13 Finishing the Arm Chair

    Lecture 14 Starting the Dining Chair

    Lecture 15 Creating the Legs of the Dining Chair

    Lecture 16 Modeling the Back of the Dining Chair

    Lecture 17 Completing the Dining Chair

    Lecture 18 Beginning the Chesterfield Sofa

    Lecture 19 Finishing the Chesterfield Sofa

    Lecture 20 Creating the Chesterfield Chair

    Lecture 21 Beginning the Desk

    Lecture 22 Adding the Drawers to the Desk

    Lecture 23 Finishing the Desk

    Lecture 24 Beginning the Desk Chair

    Lecture 25 Creating the Cushions of the Desk Chair

    Lecture 26 Modeling the Legs of the Desk Chair

    Lecture 27 Finishing the Desk Chair

    Lecture 28 Beginning the Wall Table

    Lecture 29 Creating the Drawers and Legs of the Wall Table

    Lecture 30 Finishing the Wall Table

    Lecture 31 Beginning the Side Chair

    Lecture 32 Working on the Legs of the Side Chair

    Lecture 33 Finishing the Side Chair

    Lecture 34 Starting the End Table

    Lecture 35 Completing the End Table

    Section 3: Modeling the Room

    Lecture 36 Using Walk Navigation

    Lecture 37 Blocking In the Walls and Panels

    Lecture 38 Modeling the Mantel of the Fireplace

    Lecture 39 Continuing the Fireplace

    Lecture 40 Using the Boolean Modifier

    Lecture 41 Placing the Fireplace in the Room

    Lecture 42 Creating the Door Opening and Hallway

    Lecture 43 Modeling the Door Panels

    Lecture 44 Working on the Wall Details

    Lecture 45 Adding the High Windows

    Lecture 46 Finishing the Wood Panels

    Lecture 47 Finishing the Window Frames

    Section 4: UV Mapping the Furniture

    Lecture 48 The Basic Tools of UV Mapping

    Lecture 49 UV Mapping the Coffee Table

    Lecture 50 Testing and Organizing the UV Map

    Lecture 51 UV Mapping the Round Table

    Lecture 52 Beginning the UV Map for the End Table

    Lecture 53 Finishing the End Table

    Lecture 54 UV Mapping the Wall Table

    Lecture 55 UV Mapping the Dining Chair

    Lecture 56 UV Mapping the High Back Chair

    Lecture 57 Finishing the High Back Chair

    Lecture 58 UV Mapping the Arm Chair

    Lecture 59 UV Mapping the Desk Chair

    Lecture 60 UV Mapping the Desk

    Lecture 61 Finishing the Desk

    Lecture 62 UV Mapping the Sofa

    Lecture 63 UV Mapping the Chesterfield Chair

    Section 5: Texturing the Furniture with Substance Painter

    Lecture 64 Possible Issues When Exporting

    Lecture 65 Possible Issues when Baking

    Lecture 66 Aligning the Wood Grain of the Coffee Table

    Lecture 67 Exporting Texture Maps from Substance Painter

    Lecture 68 Using Blender’s Vertex Paint Tool

    Lecture 69 Adding Textures Based on a Color ID Map

    Lecture 70 Setting Vertex Colors for the Dining Chair

    Lecture 71 Texturing the Dining Chair

    Lecture 72 Using Materials Instead of Vertex Colors

    Lecture 73 Using Paint Layers in Substance Painter

    Lecture 74 Finishing the Wall Table

    Lecture 75 Texturing the Desk Chair

    Lecture 76 Blender’s Two Sculpting Methods

    Lecture 77 Sculpting the Wrinkles of the Desk Chair

    Lecture 78 Baking a Normal Map in Blender

    Lecture 79 Using the Baked Normal Map in Substance Painter

    Lecture 80 Using a Custom UV Test Pattern

    Lecture 81 Texturing the Desk in Substance Painter

    Lecture 82 Texturing the Arm Chair

    Lecture 83 Texturing the End Table

    Lecture 84 Preparing the Sofa for Export

    Lecture 85 Texturing the Chesterfield Sofa

    Lecture 86 Painting Out Problem Areas in the Normal Map

    Lecture 87 Using Smart Materials in Substance Painter

    Lecture 88 Texturing the Small Round Table

    Section 6: UV Mapping the Room

    Lecture 89 Beginning to UV Map the Room

    Lecture 90 UV Mapping the Fireplace

    Lecture 91 UV Mapping the Walls

    Lecture 92 Finishing the Room UV Maps

    Section 7: Texturing the Room

    Lecture 93 Exporting and Baking the Room

    Lecture 94 Texturing the Room

    Lecture 95 Applying Textures to the Room

    Lecture 96 Exporting the Room Textures

    Lecture 97 Applying the Room Textures in Blender

    Section 8: Creating Accessories and Trim

    Lecture 98 Creating the Rugs

    Lecture 99 Modeling the Crown Molding

    Lecture 100 UV Mapping the Crown Molding

    Lecture 101 Texturing the Crown Molding

    Lecture 102 Using Krita to Texture the Paintings

    Lecture 103 Creating the Bookshelves

    Lecture 104 Creating Antique Books

    Lecture 105 Using Blender’s Physics Engine to Drape a Blanket

    Lecture 106 Modeling the Leaded Glass Windows

    Section 9: Lighting and Rendering the Scene

    Lecture 107 Render Settings and Emissive Materials

    Lecture 108 Practical Lights and Camera Settings

    Lecture 109 Creating the Window Glass Material

    Lecture 110 Adding Lights to the Scene

    Lecture 111 Creating Volumetric Lighting

    Lecture 112 Render Settings and Light Properties

    Lecture 113 Creating a Quick Fire Simulation

    Lecture 114 Setting Up the Scene for Rendering

    Lecture 115 Rendering an Animation

    Lecture 116 Using Blender’s Video Sequence Editor

    Lecture 117 Animating the Rotating Record Player

    Lecture 118 Adding Transitions and Rendering

    Lecture 119 Adding Music

    Lecture 120 Thank You!

    Beginner Blender users who would like to learn environment creation.

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    Coursera – Natural Language Processing Specialization by deeplearning.ai


    Coursera – Natural Language Processing Specialization by deeplearning.ai
    Video: .mp4 (1280×720) | Audio: AAC, 44100 kHz, 2ch | Size: 748.99 Mb
    Genre: eLearning Video | Duration: 6h 59m | Language: English​

    Break into the NLP space. Master cutting-edge NLP techniques through four hands-on courses!

    Natural Language Processing with Classification and Vector Spaces
    In Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:

    a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbor search. Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

    Natural Language Processing with Probabilistic Models
    In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:

    a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is important for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

    Natural Language Processing with Sequence Models
    In Course 3 of the Natural Language Processing Specialization, offered by deeplearning.ai, you will:

    a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning. Please make sure that you’ve completed Course 2 and are familiar with the basics of TensorFlow. If you’d like to prepare additionally, you can take Course 1: Neural Networks and Deep Learning of the Deep Learning Specialization. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

    For any request, feel free to send me a PM

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    Coursera – IBM RAG and Agentic AI Professional Certificate


    Last updated 2/2026 By IBM MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 110 Lessons ( 10h 24m ) | Size: 3.5 GB​

    Build real-world AI with RAG and agentic AI. Use AI tools to streamline automation, drive innovation & take your career further, faster.

    What you’ll learn
    Build the job-aligned GenAI skills and hands-on experience to create RAG, multimodal, and agentic AI applications employers need in just 3 months
    Learn to design and chain AI tools with LangChain for modular, reusable gen AI workflows
    Implement function calling, RAG, and vector stores to build intelligent, context-aware applications
    Create autonomous AI agents using LangGraph, CrewAI, and AG2 for real-world impact

    Skills you’ll gain
    Web development
    Enhance your career in the high-growth field of artificial intelligence with advanced skills in generative AI. This program is designed to equip you with advanced techniques and tools such as prompt engineering, Agentic, and multimodal AI integration, enabling you to create sophisticated & context-aware AI applications.
    This program will teach you advanced AI skills including developing efficient Retrieval-Augmented Generation (RAG) pipelines, integrating multimodal AI for text, image, and audio processing, as well as designing autonomous multiagent systems. The program will not only help you advance your career in AI but also provides a strong foundation for future career development in areas such as advanced data science, deep learning, and AI-driven automation.
    You’ll also utilize the latest tools used by AI experts, including integrating external tools and APIs with AI agents, orchestrating complex workflows using LangChain and LangGraph, and frameworks like CrewAI, AG2, BeeAI, and Model Context Protocol (MCP).
    When you complete the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise.
    This program is ideal for software developers, machine learning engineers, data scientists, and anyone with Python programming experience who wants to level up their AI engineering game. Enroll today and master the latest generative AI technologies at the forefront of innovation!

    Applied Learning Project
    Throughout this program, you’ll complete hands-on projects and labs, gaining practical experience in building AI applications with generative, retrieval-augmented, and agentic techniques.
    Projects
    Develop prompt engineering techniques and LangChain prompt templates
    Build smart AI applications by empowering LLMs with LangChain
    Set up a Gradio interface to interact with your models
    Implement function calling and integrate external tools for context-aware responses
    Conduct similarity searches on text using Chroma Vector DB
    Build and utilize your own data visualization agent
    At the end of this program, you’ll complete a real-world capstone project specifically designed to showcase your newly learned AI application skills.

    Welcome to Check it Every Days
    If you have any troubles with downloading, PM me

    Happy Learning!!


    Containers and Images: The Big Picture


    Containers and Images: The Big Picture
    MP4 | Video: AVC 1280×720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours 9M | 457 MB
    Genre: eLearning | Language: English​

    Have you wondered about containers and wanted to understand the big picture of containers? If so, then you need to learn not just about containers, but also distributing images and that’s what this course will cover.

    I’m sure you’ve heard of containers, but have you heard of images? In this course, Containers and Images: The Big Picture, you’ll learn about the big picture of both. This course will help you understand why containers exist and what exactly a container is, down to the level of understanding what changes in the Operating System to support these. You’ll learn a bit of history and begin to understand the significance of images because without a means of distributing images for containers, containers wouldn’t be a big deal. You’ll learn how image technology can be brought to highly optimized VMs and other execution environments. In doing this, you’ll start to see where the future of app packaging and distribution is heading. After this, you’ll learn about some ways you can get started with the benefits of containers and images outside of production environments, and finally, you’ll explore what your infrastructure might look like when you need to scale to large capacity with hundreds or thousands of containers. By the time you’re done with this course, you’ll have a good understanding of the what and why of containers, images, and clusters.

    Configuring and Managing Kubernetes Storage and Scheduling


    Configuring and Managing Kubernetes Storage and Scheduling
    Released 10/2025
    By Patrick Rusch
    MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
    Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 4m | Size: 137 MB​

    Running apps at scale requires the right storage and scheduling. This course will teach you how to manage Kubernetes storage and influence pod placement for efficient workloads.

    Modern applications rely on Kubernetes for scalability and resilience, but without the right storage and scheduling strategies, workloads can become inefficient and unreliable. In this course, Configuring and Managing Kubernetes Storage and Scheduling, you’ll learn to configure persistent storage and manage pod placement for optimized cluster performance. First, you’ll explore how Kubernetes volumes, PersistentVolumeClaims, and StorageClasses provide reliable data storage for stateful applications. Next, you’ll discover the fundamentals of pod scheduling and how to use node selectors, affinities, and taints to influence workload placement. Finally, you’ll learn how to combine storage and scheduling strategies to handle real-world scenarios and ensure your applications remain performant and stable. When you’re finished with this course, you’ll have the skills and knowledge of Kubernetes storage and scheduling needed to confidently manage workloads in production-like environments.