• views: 110
• date: 26 September 2026
• posted by: 0nelove

HTML, CSS and jаvascript for Beginners
Yannick Gregoire | Duration: 2:58 h | Video: H264 1920x1080 | Audio: AAC 44,1 kHz 2ch | Size: 873 MB | Language: English

Learn the fundamentals of creative web design to build interactive sites from scratch using HTML, CSS, and jаvascript

Just like paint, clay, or fabric, programming languages can be a medium for expressing our creative ideas. Yannick Gregoire is a developer who operates at the crossroads between design, coding, and animation to construct functional yet artistic websites. He has worked with brands like Mitsubishi, Sony, and Canon, as well as a number of nonprofits and design studios.

In this online course, learn the fundamentals of web development using HTML, CSS and jаvascript. Yannick walks you through the process of building dynamic websites from start to finish, as well as how to use animation and interactive properties to bring them to life. Explore the creative possibilities of coding to design compelling pages.

Meet your teacher for the course, Yannick Gregoire. Discover what got him into creative web design and where he gets his daily dose of inspiration, from code to interactive interfaces.

Next, get an introduction to the world of web development. Yannick takes you through the fundamentals of HTML, CSS and jаvascript. See how websites are laid out and the tools you need to plan and develop them.

Time to design your site. Learn how to write a page structure and content with HTML. Then see how to style those elements using CSS code and bring them to life with animations and transitions.

Once you've built the framework, make it dynamic and interactive with jаvascript. Delve into the fundamentals like variables, functions, the query selector, and event listeners to optimize the user experience.

Finally, Yannick shows you how to go live with your site. He also shares some tips about the command line and techniques to improve your coding for future projects.

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• views: 71
• date: 26 September 2026
• posted by: 0nelove

How to Create Cool Textures for Digital Creations
Matt McCracken, Ross Proulx | Duration: 2:38 h | Video: H264 1920x1080 | Audio: AAC 44,1 kHz 2ch | Size: 1,14 GB | Language: English

Make your art prints stand out by creating unique and cool textures.

Matt McCracken and Ross Prew, the creative duo behind Doublenaut, will teach you how to infuse the roughness, textures, and imperfections of old-school style into your digital illustrations. With their 18 years of experience in graphic design and illustration, they have collaborated with global brands like Adidas, Saucony, and Element Skateboarding, creating iconic packaging designs for Bellwood's Brewery.

Doublenaut's work is a fusion of punk, psychedelic art, and music, combined with a passion for exploring traditional printing techniques.

In this online course, you'll embark on a creative journey with Doublenaut, the renowned studio specializing in Graphic Design and Illustration. Throughout the course, you'll discover the secrets behind bringing the roughness, textures, and imperfections of old-school style into your new digital creations. Get ready to elevate your illustrations and make them truly unique.

To begin, you'll delve into the world of inspiration by building a mood board using a variety of references, such as gig and film posters, books, and websites. Drawing from these influences, you'll learn how to brainstorm and sketch to set up your illustration, ensuring a strong foundation for your creative process.

Next, you'll dive into the exciting realm of texture techniques. Doublenaut will guide you through various methods, from shading to speckle effects, allowing you to add depth and dimension to your artwork. Discover how to create authentic halftones and overlays that will enhance the visual impact of your illustrations.

As the course progresses, you'll explore alternative methods for texture creation. Doublenaut will share their expertise in vectorizing your textures, providing you with the skills to edit and apply them to your illustrations seamlessly. You'll learn how to strike a harmonious balance between old-school techniques and your modern taste, resulting in captivating and visually stunning art prints.

Throughout the course, Doublenaut emphasizes the importance of experimentation and individuality. You'll have the freedom to adapt the techniques to your unique style, unleashing your creativity and allowing your illustrations to stand out from the crowd.

By the end of this course, you'll have honed your skills in incorporating textures into your digital illustrations. You'll be equipped with the knowledge and techniques to develop your own art print, infusing it with captivating and rich textures that will captivate your audience. Let Doublenaut guide you as you take your creative journey to new heights, leaving a lasting impression with your one-of-a-kind illustrations.

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• views: 77
• date: 26 September 2026
• posted by: 0nelove

AI Coding Agents: Claude Code, Codex, Cursor & Copilot
Published 9/2026
Created by Abay Assenov
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 47 Lectures ( 5h 50m ) | Size: 3.4 GB

GitHub Copilot, Cursor, Claude Code and Codex: how each works, where each fits, what they cost, with short exercises

What you'll learn
⚡ Understand what an AI coding agent is, and how a model, a harness, a tool call and a permission layer fit together into a loop.
⚡ See how the same kind of engineering task moves through Claude Code, OpenAI Codex, Cursor and GitHub Copilot, and where each one stops.
⚡ Tell apart the terms people use loosely: agent and autocomplete, skill and rule and hook, context window and memory, checkpoint and version control.
⚡ Understand why cost belongs per accepted change rather than per seat, and how context size, retries and tool output drive it.
⚡ Try the judgement calls yourself: triage tasks, find the faults in an instruction file, name your own blast radius, and turn a one-line ticket into a spec.
⚡ Tell a good review of agent work from a bad one, and see how agent runs fail by recognising the sign each failure leaves.

Requirements
❗ No prior experience needed.
❗ Nothing to install, and no account with any of the four tools is required.
❗ You will get more from the practice sheets if you have a repository of your own to look at.

Description

This course contains the use of artificial intelligence.
Coding agents arrived faster than the vocabulary for them. You are told that four tools are all different, and also that they are all the same thing. This course settles that question by explaining the machinery instead of selling a tool. By the end you understand what an AI coding agent is, how one is built, what words the field uses, and what these tools look like on real teams, and you have tried the judgement calls yourself on your own material.

Who this is for. Developers who want to know what is actually happening when an agent edits their repository. Tech leads who have to decide what their team standardises on, what it costs, and how the work gets reviewed. Adjacent specialists, in testing, in security, in product, in compliance, who are now in the room when these decisions get made and need the concepts rather than the keystrokes.

Who this is not for. If you want a code-along where you type what the instructor types and end up with an application, this is the wrong course. There is no screen recording here and nothing to install. If you only want a feature tour of one tool, the market already has several. And if you want someone to tell you which tool is best, you will be disappointed on purpose: the honest answer is that the architecture is shared and the choice comes down to where the work lives, how it gets reviewed, and what it costs per accepted change.
What makes this course different. Four things. First, every module follows one real, sourced case from beginning to end, including the part where something broke. Second, honesty is structural, not decorative: one whole lesson walks through a randomised trial in which experienced developers were measurably slower while feeling faster, there is a full catalogue of the ways agent runs fail, and there is a named lesson about who this way of working does not suit. Third, this is a map rather than a list. The harness-and-model frame is built once, in the early modules, and then reused on all four tools, so you see connections instead of four disconnected feature tours. Fourth, every module ends with one short judgement task with a model answer, done on work you already have.

The modules, and the case each one follows.

✨ What an AI coding agent is. The case is a large retailer handing an agent its hardest task inside a very large codebase, then rebuilding its workflows around what it learned.

✨ What an agent is made of. The case is a controlled study across ten open-source repositories that ran the same pull-request tasks with and without a committed instruction file.

✨ The words people use. The case is a vendor redesign that broke setups people had already built habits and glue around, which is where lock-in actually lives.

✨ Claude Code and OpenAI Codex on one task. The case is the Northwind Ledger authentication refactor, a composite engineering task named as composite on screen, taken through both terminal agents from the ticket to the limit each one hits.

✨ Cursor and GitHub Copilot on one task. The case is the Northwind Ledger checkout bug, the same fictional product and different work, fixed once inside the editor and once as a pull request.

✨ A working week with agents. The case is a research organisation that measured developer productivity, found the opposite of what its participants believed, and then publicly redesigned its own experiment.

✨ What comes next. The case is a private bank taking coding agents through risk, information security and data protection before a single developer got access.

That last module, lesson by lesson. It opens with the key terms for context engineering and spec-first work. Then: know whether this way of working suits you, which is the honest exit ramp. Understand context engineering and what survives it, separating the three practices that hold from the methodology noise around them. See the four agents side by side on one table, on axes chosen because they do not go stale. See what a regulated rollout has to prove first. Watch a bank take agents through compliance. Know what this course skipped and where to get it. And finally, try it: rewrite a one-line ticket as a spec, where the graded point is the list of what the agent may not touch.

What this course does not cover. No prices and no per-plan usage limits, because they change constantly and because a course that names them teaches you a number instead of a method. No benchmark rankings, since a score measures the model and the harness together. No model names. No installation walkthroughs, no configuration reference, and no screen recordings. No language or framework tutoring: the example code is there to be read, not to be run. No deep dive on writing your own tool servers. Where something was left out, the last lesson says so and says where to go for it.

You will need no prior experience with any of these four tools. You will get more out of the course if you have read other people's code before, because reviewing work you did not write is the skill this whole subject turns on.

Who this course is for
⭐ Developers who use, or are about to use, a coding agent and want to know what it is doing.
⭐ Tech leads and engineering managers choosing what a team standardises on and how that work gets reviewed.
⭐ Testers, security engineers, product people and compliance specialists who sit in these decisions without writing the code.
⭐ Anyone who has to explain coding agents to other people and wants the concepts to be right.
⭐ Not for you if you want a code-along that ends with a working application, a tutorial for one tool only, or a verdict on which tool is best.

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• views: 79
• date: 26 September 2026
• posted by: 0nelove

Total: CompTIA SecAI+ (CY0-001)
Published 9/2026
Created by Total Seminars Over 1 Million Enrollments, Michael Solomon
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 61 Lectures ( 9h 36m ) | Size: 10 GB

Securing and responsibly using artificial intelligence in a cybersecurity practice, AI governance, OWASP, MITRE ATLAS

What you'll learn
⚡ How to secure AI systems across every layer — model guardrails and hardening, access controls, encryption, and monitoring and attack analysis
⚡ How AI typesand data security controls shape the attack surface of an AI deployment, and how to apply the Model Development Lifecycle (MDLC)
⚡ How AI is used to defend and to attack — from SOC augmentation and fraud detection to deepfakes, AI-generated malware, and AI-driven reconnaissance
⚡ How to govern AI through organizational structures and roles and regulatory frameworks including the EU AI Act, NIST AI RMF, and ISO/IEC 42001

Requirements
❗ A working knowledge of core cybersecurity concepts equivalent to CompTIA Security+; no prior AI or machine learning background required.

Description
CompTIA SecAI+ (CY0-001) is CompTIA's certification focused on securing and responsibly using artificial intelligence within a cybersecurity practice. This course provides comprehensive skills and exam preparation for security analysts, SOC analysts, and other cybersecurity professionals who need to understand how AI is changing the threat landscape and how to defend AI systems, secure AI-driven workflows, and use AI as a security tool. The course covers all four SecAI+ exam domains: Basic AI Concepts Related to Cybersecurity, Securing AI Systems, AI-assisted Security, and AI Governance, Risk, and Compliance.

Learners begin with the foundational vocabulary of artificial intelligence and machine learning security (AI types, model training, prompt engineering, and data security) before moving into the technical core of the course: AI threat modeling with the OWASP LLM Top 10 and MITRE ATLAS, model guardrails and hardening, access controls, encryption, and monitoring and attack analysis across every layer of an AI deployment. The course then covers how AI is used both defensively and offensively in security operations, from SOC augmentation and automated penetration testing to deepfakes, AI-generated malware, and AI-driven reconnaissance. It closes with AI governance, the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001, and third-party AI vendor risk.

By the end of this course, learners will be able to secure, govern, and responsibly use AI systems in a cybersecurity context, a fast-growing skill set as organizations race to adopt AI while managing new categories of risk. CompTIA SecAI+ certification demonstrates to employers that a security professional can evaluate AI-related threats, harden AI deployments, and support AI governance programs, making it a valuable credential for cybersecurity professionals looking to specialize in one of the field's fastest-emerging areas.

Who this course is for
⭐ Intermediate learners who want to dive into the world of Security related AI systems.

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• views: 46
• date: 26 September 2026
• posted by: 0nelove

Post Exploitation with Kali Linux
Released 9/2026
By Sunil Gupta
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 1m 19s | Size: 238.7 MB

When a system is breached, it is essential for professionals to acquire knowledge about the access and risks that result from the system breach.

When a system is breached, it is essential for professionals to acquire knowledge about the access and risks that result from the system breach.
This course, Post Exploitation with Kali Linux , will familiarize you with practical aspects of performing post-exploitation in a safe lab. First, you will explore system enumeration and privilege escalation.
Next, you will observe persistence and anti-detection from the perspectives of both attackers and defenders.
Finally, you will learn credential exposure, network pivoting, and Active Directory relationships to fully understand how the compromised system works.
When you are finished with this course, you will be able to present familiarity with the main post-exploitation processes.

https://www.uploadcloud.pro/hp00ym1vg9h9/Post_Exploitation_with_Kali_Linux.rar.html

https://rapidgator.net/file/d4103e157c389b080ff0778dff77b94a/Post_Exploitation_with_Kali_Linux.rar.html

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• views: 82
• date: 26 September 2026
• posted by: 0nelove

ZerotoMastery - MLOps Bootcamp: Build Real-World AI Infrastructure
Released 9/2026
Taught by Patrik Szepesi
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 68 Lessons ( 8h 58m ) | Size: 1.9 GB

MLOps is the discipline that combines AI/Machine Learning, Data Engineering, and DevOps to reliably deploy, monitor, and maintain AI models. You'll learn to go beyond model training and learn how real AI systems operate in production by connecting AWS services into a modern MLOps stack that take an AI model from experimentation to a live, continuously improving, production-ready AI application that scales.

What you'll learn
Understand the full MLOps lifecycle from training to retraining
Fine-tune Vision Transformers with Hugging Face on AWS
Prepare S3 datasets for cloud-based model training
Deploy real-time AWS endpoints for live predictions
Monitor model confidence and activity with CloudWatch
Build AWS Pipelines for automated ML workflows
Connect Lambda, API Gateway, S3 and Bedrock
Retrain models automatically and gate production deployments
You probably already know that one of the most in-demand skills in the technology workplace right now is being able to train and train and fine-tune an AI/ML model.

But having the skills to deal with what happens after that is what will actually make you indespensible and advance your career.

Those skills are building and managing the infrastructure and systems that the AI needs to be deployed, to continually improve, and to scale with a company's needs.

In this MLOps Bootcamp you'll learn exactly those skills!

You'll build the systems that take an AI/ML model from experimentation to a live, continuously improving, production-ready AI application that scales.

You'll fine-tune a Vision Transformer with Hugging Face and SageMaker, deploy it behind a real-time endpoint, capture prediction data and monitor model behavior with CloudWatch.

You'll build real-world skills with Lambda, API Gateway, S3 and Bedrock to collect low-confidence predictions, label new examples and feed them back into an automated retraining workflow.

You'll also build evaluation and deployment gates so a newly trained model doesn't automatically replace the production model just because it's newer. It has to perform better first.

By the end, you'll understand how the pieces of a modern MLOps stack connect and be ready to build the AI infrastructure needed for now and the future.

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• date: 26 September 2026
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