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Understanding Ai-Assisted Development
Released 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 31 Lessons ( 1h 33m ) | Size: 209 MB [/center]
Learn the skills and workflows you and your team need to build faster and better with AI, while avoiding its many dangers.
Guide, Judge, and Correct AI-Generated Code
The software development industry always changes. But nothing compares to what's happening right now with AI-assisted development.
There a lot of AI users now who copy prompts and hope for the best. There are also worried devs, who keep hearing that AI will replace them or that they'll lose their skills if they lean on it too much.
A proper view of AI is that it isn't a replacement for understanding. It's a tool that amplifies it. But it can also amplify you and your team's worst qualities.
The real skill isn't using AI. It's knowing how to think with it.
This course does not teach you the latest AI tool, because by next month there'll be a new one. Instead, I want to build something more permanent in your mind: a framework for understanding how large language models actually work, so you can then follow workflows and best practices that are strategic, reliable, and yours.
This course will teach you and your team how to successfully integrate LLMs across your entire software development lifecycle.
What Makes This Different
Most AI courses focus on prompts. Copy this, paste that, hope it works. But that's not how the best AI-assisted developers operate. You need to understand the underlying principles, the same way understanding JavaScript's execution context makes you better at debugging, or knowing how React's reconciliation works helps you write more performant components.
This course goes under the hood, hype-free. We'll explore the math and linguistics that make LLMs tick. You'll learn about vector spaces, attention mechanisms, and why models confabulate (what people incorrectly call "hallucinations"). We'll look at context engineering, not just prompt engineering, because managing what the model "knows" is more important than how you ask.
And yes, we'll cover practical workflows: planning, implementation, debugging, code reviews. But you'll understand why these workflows work, which means you can adapt them as tools change.
This Is For You If.
You're a beginner who wants to use AI without feeling like you're cheating yourself out of learning. You'll discover how to leverage AI while still building the mental models that make you valuable as a developer.
You're an experienced developer who wants to integrate AI into your workflow (or your team's) without sacrificing code quality. You'll learn where AI excels, where it fails, and how to structure your development process to get consistent, maintainable results.
You're worried about becoming dependent on AI or losing your edge. I address this directly. I'll show you how to use AI as a force multiplier for your expertise, not a crutch that weakens it, and how to avoid both the personal and professional pitfalls of AI-assisted development.
What You'll Walk Away With
By the end, you'll have a durable mental model of how LLMs work. You'll know how to engineer context and prompts strategically. You'll understand human-AI collaboration patterns that actually work in production codebases.
Most importantly, you'll stop feeling uncertain. You'll approach AI tools with clarity, purpose, and the confidence that comes from genuine understanding.
Because at the end of the day, the developers who thrive aren't the ones who found the best prompts. They're the ones who understand what they're doing.
In the age of AI it's even more important to not imitate, but understand.
Understanding Ai-Assisted Development
Released 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 31 Lessons ( 1h 33m ) | Size: 209 MB [/center]
Learn the skills and workflows you and your team need to build faster and better with AI, while avoiding its many dangers.
Guide, Judge, and Correct AI-Generated Code
The software development industry always changes. But nothing compares to what's happening right now with AI-assisted development.
There a lot of AI users now who copy prompts and hope for the best. There are also worried devs, who keep hearing that AI will replace them or that they'll lose their skills if they lean on it too much.
A proper view of AI is that it isn't a replacement for understanding. It's a tool that amplifies it. But it can also amplify you and your team's worst qualities.
The real skill isn't using AI. It's knowing how to think with it.
This course does not teach you the latest AI tool, because by next month there'll be a new one. Instead, I want to build something more permanent in your mind: a framework for understanding how large language models actually work, so you can then follow workflows and best practices that are strategic, reliable, and yours.
This course will teach you and your team how to successfully integrate LLMs across your entire software development lifecycle.
What Makes This Different
Most AI courses focus on prompts. Copy this, paste that, hope it works. But that's not how the best AI-assisted developers operate. You need to understand the underlying principles, the same way understanding JavaScript's execution context makes you better at debugging, or knowing how React's reconciliation works helps you write more performant components.
This course goes under the hood, hype-free. We'll explore the math and linguistics that make LLMs tick. You'll learn about vector spaces, attention mechanisms, and why models confabulate (what people incorrectly call "hallucinations"). We'll look at context engineering, not just prompt engineering, because managing what the model "knows" is more important than how you ask.
And yes, we'll cover practical workflows: planning, implementation, debugging, code reviews. But you'll understand why these workflows work, which means you can adapt them as tools change.
This Is For You If.
You're a beginner who wants to use AI without feeling like you're cheating yourself out of learning. You'll discover how to leverage AI while still building the mental models that make you valuable as a developer.
You're an experienced developer who wants to integrate AI into your workflow (or your team's) without sacrificing code quality. You'll learn where AI excels, where it fails, and how to structure your development process to get consistent, maintainable results.
You're worried about becoming dependent on AI or losing your edge. I address this directly. I'll show you how to use AI as a force multiplier for your expertise, not a crutch that weakens it, and how to avoid both the personal and professional pitfalls of AI-assisted development.
What You'll Walk Away With
By the end, you'll have a durable mental model of how LLMs work. You'll know how to engineer context and prompts strategically. You'll understand human-AI collaboration patterns that actually work in production codebases.
Most importantly, you'll stop feeling uncertain. You'll approach AI tools with clarity, purpose, and the confidence that comes from genuine understanding.
Because at the end of the day, the developers who thrive aren't the ones who found the best prompts. They're the ones who understand what they're doing.
In the age of AI it's even more important to not imitate, but understand.
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https://rapidgator.net/file/b18d9f0fc45a...t.rar.html