Welcome to Hell (Why You Can't Ignore This Anymore)

At this point, I'd like to take a moment to speak to you about Generative AI. AI is not a good tool. AI is not even a bad tool. Calling it such would be an insult to other bad tools, such as Clippy or BonziBuddy. No, AI is an abysmal stochastic parrot. Having spent several thousand dollars only to debug its hallucinations for weeks, my hate for AI has grown to a raging fire that burns with the fierce passion of a million suns.

Table of Contents

• You Don’t Want to Be Here
• What This Course Actually Is
• Why You Actually Can’t Ignore This
• What AI Actually Is
• What AI Is Actually Good For
• The Three Goals of This Course
• What Happens Next

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You Don’t Want to Be Here

Let’s get one thing straight: you didn’t sign up for this. You became a developer because you like solving problems with code, not because you wanted to chase the latest hype cycle. You’ve seen trends come and go - blockchain, NFTs, Metaverse, Web3 - and you’ve wisely sat most of them out.

AI feels like more of the same bullshit.

And you know what? You might be right.

But here’s the problem: this time it’s different. Not because AI is some kind of magic, and not because it’s going to replace all developers, but because everyone else thinks it will.

Your company brags to be AI-enabled. Your next interviewer will ask about AI. Your current manager already is. And whether you like it or not, the people using AI tools are shipping faster than you.

You don’t need to love AI. You don’t even need to like it. You just need to know enough to not become unemployable. At least until (hopefully) the AI bubble bursts in flames.

What This Course Actually Is

This is not a course for AI enthusiasts. This is not a course that will make you excited about the future. This is a survival guide for developers who have better things to do but are terrified of getting left behind.

We’re going to cover:

• The absolute minimum you need to know about how LLMs work (so you don’t sound clueless)
• How to actually use AI tools to be more productive (even if you hate admitting they help)
• How to build one AI feature you can talk about in interviews (because “no” is not an acceptable answer to “have you worked with AI?”)

We’re not going to:

• Pretend this is revolutionary technology that will change everything
• Make you do math
• Build anything that requires a PhD, a GPU cluster, or selling your firstborn for cloud credits
• Waste your time with hype

Why You Actually Can’t Ignore This

Reason 1: The Interview Question You Can’t Dodge

“So, have you worked with any AI or LLMs?”

You can see it coming. Every tech interview in 2026 includes this question. You can’t say “no, I think it’s overrated” because that sounds like “no, I haven’t kept up with technology.” You can’t say “I’ve played around with ChatGPT” because everyone has. You need a real answer.

Reason 2: The Tools Are Already Here

GitHub Copilot. ChatGPT. Claude. Cursor. Your company probably already pays for at least one of these. Your teammates are using them. And they aren’t necessarily smarter - they just have less baggage about “doing it the right way.”

Meanwhile, you’re still Googling Stack Overflow answers from 2015, and ignoring the big tech layoffs.

Reason 3: The Job Market Shifted While You Weren’t Looking

Remember when “knows Git” became a baseline requirement? Then “understands Docker”? Then “has worked with cloud providers”? Each time, there was a period where you could ignore it, until suddenly you couldn’t.

We’re in that window with AI right now. In 12-18 months, “comfortable with LLMs” will be table stakes. The devs who refused to learn Git eventually had to learn it or leave. Don’t be that person.

Reason 4: Your Manager Already Thinks You Should Know This

Even if they haven’t said it out loud yet. The next time there’s a discussion about “should we add AI to this feature?” and you have nothing to contribute except skepticism, you’re going to feel it. You don’t need to be the AI evangelist, but you can’t afford to be the AI denier.

I know a manager who used to casually say “you need an MCP for that” when people were struggling with issues. It was funny until it wasn’t.

What AI Actually Is (Spoiler: Regex on Solar System Scale)

Here’s the thing nobody wants to tell you: AI - specifically Large Language Models - is not that complicated. It’s just really good autocomplete. It predicts the next word based on patterns it saw in training data. That’s it.

All the magic, all the hype, all the “emergent capabilities” - it’s just really, really good pattern matching at scale.

Does it sometimes seem intelligent? Sure. Is it? No. It’s a very expensive parrot that’s read the entire internet and is really good at remixing what it’s seen.

Understanding this will save you from two common mistakes:

1. Overestimating it: No, it can’t build your entire app. No, it won’t replace you. No, it doesn’t “understand” your code.
2. Underestimating it: Yes, it can help you debug faster. Yes, it can generate boilerplate. Yes, it’s actually useful despite being “just autocomplete.”

What AI Is Actually Good For

Let’s cut through the bullshit and talk about what LLMs are genuinely useful for in your day-to-day work:

Things AI is surprisingly good at:

• Explaining error messages in plain English
• Writing boilerplate code you don’t want to type
• Generating test cases you were too lazy to write
• Translating between programming languages
• Explaining legacy code nobody understands
• Rubber duck debugging (it’s surprisingly good at “have you tried…?”)
• Writing regex patterns (because nobody remembers regex syntax)

Things AI is confidently terrible at:

• Security-critical code (it will confidently suggest vulnerabilities)
• Anything requiring real-time information
• Understanding your specific business logic
• Remembering context from 10 minutes ago
• Admitting when it doesn’t know something
• Not hallucinating APIs that don’t exist

The trick is learning which is which.

The Three Goals of This Course

Goal 1: Survive the AI FOMO

Everyone is talking about AI. Every company is “exploring AI initiatives.” Every startup is “AI-powered.” It’s exhausting. This course will give you enough knowledge to nod along in meetings, ask reasonable questions, and not feel like you’re drowning in buzzwords.

Goal 2: Actually Be More Productive

Yes, really. Despite your skepticism, some AI tools can save you time because they’re good at the boring shit you don’t want to do. By the end of this course, you’ll have integrated AI into your workflow in ways that feel natural, not forced.

Goal 3: Have Something to Talk About in Interviews

You need to be able to say “yes, I’ve built something with AI” and not be lying. You need a portfolio project, a GitHub repo, something real you can point to. We’ll build that together. It won’t be revolutionary, but it’ll be enough.

What Happens Next

In the next module, we’ll cover the bare minimum you need to know about how LLMs work. Not the math, not the theory, just enough to not sound like an idiot when someone mentions “tokens” or “context windows.”

After that, we’ll get practical. Prompting techniques, code generation, building actual features. No fluff, no hype, just the stuff you need to know.

By the end of this course, you won’t be an AI expert. You won’t be building the next ChatGPT. But you’ll be able to use AI tools effectively, you’ll have built something real, and you won’t be terrified every time someone asks “so what do you think about AI?”

One Last Thing

You’re allowed to still think this is overhyped. You’re allowed to be skeptical. You’re allowed to roll your eyes at the breathless “AI will change everything” takes.

But learn it anyway.

Not because you believe the hype, but because the market believes it, and you need to eat.

Lasciate ogni speranza, voi ch’entrate.

Welcome to hell. Let’s get this over with.