Welcome to the Course

The promise: a path, not a pile of tips

If you've ever searched "how to get into FAANG," you've seen the internet's version of advice: a thousand disconnected hot takes. "Grind 500 LeetCode problems." "Just network." "Your résumé needs to fit on one page." "Actually, two pages is fine now." Each tip might be true, but a heap of tips is not a plan. You're left guessing what to do first, what matters most, and whether you're even making progress.

This course is different in one specific way: it's sequential and complete. Every lesson builds on the one before it, and together they cover the entire journey — from understanding the industry, to mastering data structures and algorithms, to building a portfolio, to walking out of the final round with an offer in hand. You will never be left wondering "okay, but what do I do now?" The next step is always the next lesson.

Here's a concrete picture of what "complete" means. By the time you finish, you should be able to:

• Look at an unfamiliar coding problem and methodically work toward a solution out loud, the way an interviewer wants to see.
• Sketch the high-level design of a system like a URL shortener or a news feed and explain your trade-offs.
• Hand someone a résumé that gets you called, not filtered out by a keyword scanner.
• Tell a clear, structured story about your past work when an interviewer asks "tell me about a time when…"
• Apply strategically, ask for referrals without feeling awkward, and negotiate an offer instead of just accepting the first number.

Who this course is for (and who it isn't)

Let's be honest about fit, because the right expectations make everything that follows easier.

This course is for you if you're a college student wondering how people actually break into big tech, a recent graduate staring down your first real job search, a self-taught developer who can build things but has never "done interviews," or a working engineer who wants to level up to a more competitive company. The common thread isn't your background — it's that you're willing to do the work and want a clear map for doing it.

You need exactly one prerequisite to start: basic comfort with a single programming language. If you can write a loop, an if-statement, and a simple function in Python, Java, JavaScript, C++, or any mainstream language, you have enough. Everything else — data structures, algorithms, system design, résumé craft, behavioral interviewing — is built up from the ground floor inside this course. You do not need a computer science degree. You do not need prior interview experience. You do not need to already know what a hash map is.

Here's a concrete example of the journey. Meet Maya, a second-year CS student. Right now she can write a for loop, but the words "binary tree" make her freeze — she's heard them, she knows they're important, and she has no idea where to begin. By the end of this course, Maya can draw a binary tree on a whiteboard, explain what it's good for, code a traversal of it, and narrate her thinking the entire time so an interviewer can follow along. She didn't get there by being a genius. She got there one small, ordered step at a time — which is exactly how this course is built.

This course is probably not for you if you're hunting for a single magic trick that skips the work, or you want an overnight result. There isn't one, and anyone selling you one is selling you something. Real preparation takes weeks to months. The good news: it's finite and it's learnable, and that's the whole reason this course exists.

The three pillars of getting hired

Most people assume landing a top tech job is purely about coding ability. It isn't — and that single assumption is why so many talented engineers stay stuck. Top companies actually evaluate three different things, and a weakness in any one of them can sink an otherwise excellent candidate. Think of your offer as a roof held up by three pillars.

Pillar 1 — Skills

Skills is your raw technical ability, and it's the pillar people obsess over. It splits into two parts. The first is coding: data structures and algorithms, the bread and butter of nearly every technical interview. Can you reach for the right tool — a hash map, a stack, a binary search — and implement it cleanly under a little pressure? The second is system design: given a vague prompt like "design Twitter," can you reason about scale, storage, and trade-offs at a whiteboard? We devote entire sections to both, starting from absolute fundamentals.

Pillar 2 — Story

Story is how you present who you are. It's the part most engineers neglect, and it's responsible for an enormous share of silent rejections. Your résumé is the first thing a recruiter sees — often for six seconds before deciding to read on or move on. Your projects are the proof that you can build, not just solve textbook puzzles. And your behavioral answers — your responses to "tell me about a time you disagreed with a teammate" — decide whether people want to actually work with you. A great coder with a weak story gets filtered out before anyone sees the code.

Pillar 3 — Strategy

Strategy is the game around the game — the moves that decide whether your skills and story ever get a fair hearing. It includes how you apply (a referral can be worth more than a hundred cold applications), how you time and target your search, and how you negotiate once an offer arrives. Many candidates leave real money and better-fitting roles on the table simply because nobody taught them this pillar exists. We'll fix that too.

How to use this course (so you actually finish)

Here's an uncomfortable truth about online learning: most people who start a course never finish it. They watch a few lessons, feel productive, drift away, and quietly give up. We're going to set you up to be the exception, and it comes down to a handful of habits.

Go in order, at least the first pass. Later sections lean on earlier ones — the system design material assumes you know your data structures; the negotiation material assumes you understand the hiring funnel. Skipping ahead feels faster but usually costs you time when you hit something you weren't prepped for.

Do the exercises — don't just read them. Reading about push-ups doesn't build muscle, and reading about algorithms doesn't build problem-solving. Every lesson with a hands-on exercise is asking you to do a small rep. Those reps are where the actual learning lives. The difference between "I understand this" and "I can do this under pressure" is entirely the practice.

Don't skip the "boring" parts. The résumé, the applications, the outreach — these feel less glamorous than solving a clever algorithm, but they're often where the biggest and fastest wins hide. Improving a résumé can flip your interview rate overnight. That's a better return than your hundredth practice problem.

Mechanically, the site is built to keep you on track:

• Follow the sidebar on the left — it lays out every section and lesson in order and tracks what you've completed.
• Mark each lesson complete with the button at the very bottom of the page. Watching your progress bar fill up is a surprisingly strong motivator.
• Use the quizzes at the end of each lesson to check yourself honestly before moving on. If you miss one, that's a signal to reread, not to feel bad.
• Take the reflection prompts seriously. Thirty seconds of honest thinking turns passive reading into a plan.

Further reading

• Blind — an anonymous community where tech employees discuss interviews, offers, and compensation.
• Levels.fyi — crowdsourced compensation and leveling data across major tech companies, useful context for the Strategy pillar.