Speaker 1: Welcome, Welcome to the Deep Dive. Today, we're plunging into
Speaker 1: a topic that's well, it's pretty make or break for
Speaker 1: a lot of tech careers, but it's also just a
Speaker 1: really fascinating exercise in thinking, you know, structured thinking, problem solving,
Speaker 1: even communication. If you've ever felt buried under just tons
Speaker 1: of information, wondering how to pull out what really matters, well,
Speaker 1: this deep Dive it's for you. Our goal here is simple,
Speaker 1: cut through the noise, give you a shortcut to being
Speaker 1: really well informed fast. And for this journey, we're leaning
Speaker 1: on a real authority cracking The Coding Interview, sixth edition,
Speaker 1: Gail Lackman McDowell. I mean she's not just an author,
Speaker 1: founder CEO of careercup dot com. She's been deep in
Speaker 1: the tech world for years, teaching computer science, prepping engineers
Speaker 1: for some seriously tough interviews, you know, the big ones.
Speaker 1: So her insights they come from real experience. She knows
Speaker 1: what these top companies are actually looking for.
Speaker 2: That's absolutely right, and our mission today really is to
Speaker 2: pull out those core strategies, the mindsets you need not
Speaker 2: just to get through technical interviews, but to genuinely excel.
Speaker 2: And what's so interesting, and why this is relevant beyond
Speaker 2: just tech is that these principles they're not just about code.
Speaker 2: They're really fundamental problem solving techniques, logical thinking, critical analysis
Speaker 2: skills that honestly are gold in almost any field or
Speaker 2: you know, dealing with complex stuff life throws at you.
Speaker 2: So we're going to unpack that give you some things
Speaker 2: you can actually use.
Speaker 3: Okay, let's kick things off with a question. Pretty much
Speaker 3: everyone asks. The book tackles it head on. Why interview
Speaker 3: this way?
Speaker 2: Log?
Speaker 3: You hear the complaints all the time, right, great candidates
Speaker 3: sometimes bomb these things. In the real world, you'd look
Speaker 3: stuff up, you'd talk to people, and yeah, coding on
Speaker 3: a whiteboard it feels artificial. What's the book's take on
Speaker 3: all that?
Speaker 2: Yeah, it's a really fair point. Honestly, the author kind
Speaker 2: of agrees with a lot of those complaints. The process
Speaker 2: isn't perfect, yea far from it, but understanding where the
Speaker 2: interviewer is coming from that's absolutely crucial. What's interesting is
Speaker 2: that it's not meant to be about trivia, about just
Speaker 2: memorizing facts, even though yeah, I can feel that sometimes
Speaker 2: it's designed really to get at those core problem solving skills.
Speaker 2: Algorithmic thinking, critical analysis. These are things that are just
Speaker 2: really really hard to teach someone once they're on the job.
Speaker 2: So companies are looking for that baseline aptitude, you know,
Speaker 2: a way of thinking that suggests you can learn and grow,
Speaker 2: not just what you already know or can google fast.
Speaker 1: So it's about seeing how they think fundamentally, not just
Speaker 1: a knowledge test. But isn't there a risk there that
Speaker 1: maybe brilliant people who just get nervous in that setting
Speaker 1: they get missed.
Speaker 2: Oh? Absolutely, that's a known issue and the book doesn't
Speaker 2: shy away from that. It's a challenge. But from the
Speaker 2: company's angle, these interviews are like a filter necessary maybe
Speaker 2: even if I'm perfect, They're trying to find people with
Speaker 2: strong foundational skills. It's a trade off. Basically, they want
Speaker 2: to minimize hiring mistakes, false positives, even if it means
Speaker 2: some good people false negatives might slip through. It really
Speaker 2: just highlights why as a candidate you need to understand
Speaker 2: the game. You know what they're looking for.
Speaker 1: That makes sense. It's a filtering mechanism, flaws and all.
Speaker 1: So moving on to the kinds of questions, the book
Speaker 1: really digs into algorithms, coding, design, problems. That's the main focus.
Speaker 1: It mentions behavioral questions trivia, but they're not the core
Speaker 1: emphasis for prepping. Why is that exactly?
Speaker 2: You can sort of group them into two main buckets. First,
Speaker 2: problem solving questions. These are the tough ones, usually involving
Speaker 2: data structures, algorithms, maybe system design. They're testing your deep
Speaker 2: problem solving chops, especially if those skills are like day
Speaker 2: to day requirements for the job, how you handle something new.
Speaker 2: Then you've got specialist questions. These are different. They go
Speaker 2: deep into specific areas like you know, Java garbage collection details,
Speaker 2: or specific machine learning models or network protocols. They use
Speaker 2: those when a very specific specialized skill is absolutely essential
Speaker 2: and it's not something you can just pick up quickly.
Speaker 2: But the book focuses on the problem solving stuff because
Speaker 2: it's more universal. Often the bigger hurdle.
Speaker 1: For people, right the transfer skills. So if you're prepping,
Speaker 1: what's the absolute must know stuff the book calls out
Speaker 1: It lists quite a few foundational topics.
Speaker 2: Yeah, it's a solid list. You've got your core data structures,
Speaker 2: link lists, trees, tries that's tries, graphs, stacks, queues, heaps,
Speaker 2: vectors or arraylists and hash tables crucial stuff, and then
Speaker 2: the algorithms breadth first search, depth first search, binary search, merge, sort,
Speaker 2: quick sort, recursion, dynamic programming, and of course understanding BIGO
Speaker 2: for time and space complexity, plus memory concepts like stack
Speaker 2: versus heap and even bit manipulation. Now, for all of these,
Speaker 2: the key thing isn't just knowing the definition, it's knowing
Speaker 2: how to use them, how to implement them, what are
Speaker 2: their mechanics and critically, what are their time and space
Speaker 2: trade offs? Like a trigrade for prefix matching, you need
Speaker 2: to be able to visualize it, discuss when it's better
Speaker 2: than say a hash map.
Speaker 1: That deeper practical understanding. It's not just flashcards exactly.
Speaker 2: It's about having them in your toolbox, ready to go
Speaker 2: when you need them.
Speaker 1: Okay, let's unpack the house. The book gives this really clear,
Speaker 1: step by step strategy for tackling a problem, especially under pressure.
Speaker 1: A seven step method sounds useful.
Speaker 2: Oh, it's absolutely a life saver for keeping your thoughts organized.
Speaker 2: Step one, create a concrete example. This is so important.
Speaker 2: A common trap picking an example that it's too simple,
Speaker 2: too small, or just too perfect. It hides the tricky
Speaker 2: parts you need something specific, big enough to matter, and
Speaker 2: definitely not a special case. Like if the problem involves
Speaker 2: a list of numbers, don't just use one, two to three.
Speaker 2: Try something like mid five, zero, seven, two twenty, or
Speaker 2: an empty list or duplicates. You want an example that
Speaker 2: makes you think about edge cases constraints, helps you debug
Speaker 2: your own logic before you even write code.
Speaker 1: Right, No tiny, neat examples. Get something a bit messy,
Speaker 1: something realistic that pushes the boundaries makes total sense precisely.
Speaker 2: Step two, develop a brute force solution. Just get something working, anything,
Speaker 2: even if it's incredibly slower and inefficient, and state his
Speaker 2: time and space complexity right away. Why Because it confirms
Speaker 2: you understand the requirements. It gives you a starting point,
Speaker 2: and it shows you're making progress. It's like a drawing
Speaker 2: a quick map on a napkin. It's not perfect, but
Speaker 2: it gets you oriented.
Speaker 1: And then from that natcin map you optimize. This sounds
Speaker 1: like where the clever thinking comes in.
Speaker 2: It really is, and a great technique here is look
Speaker 2: for BUD that stands for bottlenecks, unnecessary work and duplicated work.
Speaker 2: Bottlenecks of the slow parts. Unnecessary work is stuff you're
Speaker 2: calculating that you don't need duplicated work is doing the
Speaker 2: same calculation over and over. Take that classic problem, find
Speaker 2: pairs A, B and CD such that A three plus
Speaker 2: B three you will see three plus D three to
Speaker 2: the naive way four nest of loose. That's o N
Speaker 2: four pretty slow. But if you optimize, maybe using a
Speaker 2: hash table, I think about it. You could calculate all
Speaker 2: the C three plus D three sums first, store them
Speaker 2: in a hash table key is the sum value as
Speaker 2: a list of CD pairs. That takes O N two time.
Speaker 2: Then you calculate all the A three plus B three
Speaker 2: sums another o N two and for each one you
Speaker 2: just look up the sum of your hash table. That
Speaker 2: look up is super fast, basically one on average. So
Speaker 2: the whole thing drops from four down to two. Huge difference.
Speaker 2: It shows the power of the right data structure.
Speaker 1: Wow, okay, four to N two. That's a massive improvement.
Speaker 1: That really drives home my understanding data structures isn't just academic,
Speaker 1: it's practical power. It's like finding a shortcut nobody else saw. Okay,
Speaker 1: so you've got this optimized approach, what's next to make
Speaker 1: sure it's solid?
Speaker 2: Good question? Once you have that optimized idea. Step four
Speaker 2: is crucial walk through it. Use that concrete example you
Speaker 2: made earlier. Go step by step, show how variables change,
Speaker 2: how that data flows. Don't just talk about it, simulate it.
Speaker 2: This proves you understand it deeply and helps catch flaws.
Speaker 2: After that, step five, implement it. And the goal isn't
Speaker 2: just code that works. It's beautiful code that means thinking
Speaker 2: about structure. Modularize it, break it into functions, use clear
Speaker 2: variable names. Explain your choices, maybe why you refactored something.
Speaker 2: It shows good software engineering habits, not just getting the answer.
Speaker 1: Beautiful code like that phrasing. It suggest graftsmanship, right clarity,
Speaker 1: maintain exactly.
Speaker 2: It shows you care about quality. Then step six cast
Speaker 2: your code super important, just like real development. Don't just
Speaker 2: assume it works. The book emphasizes the smart testing. Use
Speaker 2: your initial example, yes, but also test edge cases, empty inputs,
Speaker 2: single items, really big numbers, really small ones. Debug carefully
Speaker 2: if something breaks, and talk through your debugging process. They
Speaker 2: want to see how you fix things too. And finally
Speaker 2: step seven, always think about special cases and edge cases,
Speaker 2: null inputs, empty lists, maximum values, off by one errors.
Speaker 2: These are often where algorithms break down. Addressing them makes
Speaker 2: your solution robust. It shows thoroughness.
Speaker 1: I mean, so that's a fantastic roadmap for building a
Speaker 1: solid solution. But how do we judge how good it is?
Speaker 1: How efficient? This is where big O comes in, right,
Speaker 1: I hear biggo all the time. What exactly is it measuring?
Speaker 1: Why is it so critical?
Speaker 2: Excellent question, because yeh, big O is fundamental. Basically, big
Speaker 2: O notation tells you about the rate of increase of
Speaker 2: your algorithm's runtime or maybe its memory usage. Is the
Speaker 2: input size gets bigger, It's not about the exact time
Speaker 2: in seconds, it's about how it scales. You double the
Speaker 2: inputs as the work double that's linear, O in does
Speaker 2: a quadruple that's quadratic O N two makes a huge
Speaker 2: difference for large inputs. We often talk about worst case time,
Speaker 2: sometimes expected case. Best case is less common in interviews.
Speaker 2: Often worse than expected are pretty much the same anyway,
Speaker 2: So think about common run times. OH one is constant
Speaker 2: time doesn't matter how big the input is, like getting
Speaker 2: an item from an array by its index instant oh
Speaker 2: log in logarithmic, super efficient thik, binary search on a
Speaker 2: sort of list, or calculating powers of two ohn is
Speaker 2: linear work grows directly with input size, like calculating factorial
Speaker 2: O N log in is common for good sorting algorithms
Speaker 2: like merged sort O N two quadratic often pops up
Speaker 2: with nested loops. You might see os co rtn like
Speaker 2: checking if a numbers prime by testing divisors up to
Speaker 2: its square root. And then there's the scary one O
Speaker 2: two n exponential like a simple recursive Fibonacci. It's incredibly slow,
Speaker 2: very fast. The main thing the book stresses is run
Speaker 2: time is not a multiple choice question. You have to
Speaker 2: not guess, count the operations, figure out what dominates is
Speaker 2: in grows, drop the constants. That's how you really understand it. Derive,
Speaker 2: don't guess, got it. And then there's this idea of
Speaker 2: best conceivable runtime BCR. You said it's not a formal term,
Speaker 2: but sounds like a useful benchmark in an interview.
Speaker 1: It really is. Yeah, think of DCR as the absolute
Speaker 1: theoretical minimum runtime for a given problem you simply cannot
Speaker 1: do better. For example, finding the biggest number in an
Speaker 1: unsorted array, you have to look at every single element
Speaker 1: at least once, right, So the BCR is O n
Speaker 1: You can't magually do it in O logan or one
Speaker 1: because you don't know anything about the order until you
Speaker 1: look another one finding common elements and two sorted arrays.
Speaker 1: Brute force might be O N two. But the best
Speaker 1: you can possibly do is on. You can achieve that
Speaker 1: by walking through both arrays together, kind of like merging them. Now,
Speaker 1: if you hit that BCR and you do it using
Speaker 1: only one extra space, meaning your memory use doesn't grow
Speaker 1: with the input size, that's a strong signal. It tells
Speaker 1: you and the interviewer that you've probably optimal as much
Speaker 1: possible in terms of time and space complexity. You're likely done.
Speaker 1: That's a really powerful indicator knowing when you've hit the
Speaker 1: theoretical limit. Okay, so we've gone deep into the technical side,
Speaker 1: the algorithms, the big O. But what about actually landing
Speaker 1: the interview in the first place and dealing with those
Speaker 1: sometimes tricky behavioral questions. There's more to it than just.
Speaker 2: Code absolutely getting the interview a step zero for resumes
Speaker 2: the message needs to be clear, I'm smart and I
Speaker 2: can code. The book recommends a really effective format for
Speaker 2: accomplishments accomplished X by implementing Y, which led to z
Speaker 2: so not just improved rendering speed, but reduced object rendering
Speaker 2: time by seventy five percent by implementing distributed caching, which
Speaker 2: led to a ten percent drop and log in time.
Speaker 2: See clear Impact for students' recent grants. That project section.
Speaker 2: It's huge. Aim from maybe two to four solid projects.
Speaker 2: List the tech you use, languages, framework, safe with solo
Speaker 2: or team, and honestly building stuff just because you're interested.
Speaker 2: That's incredibly impressive. Shows real passion and initiative. Oh and
Speaker 2: resumes one page. If you have under ten years experience,
Speaker 2: especially in the US, keep it concise and maybe be
Speaker 2: careful about looking too focused on just one language, like
Speaker 2: say dot net. If you're going for general software roles,
Speaker 2: you might need to frame it to show broader problem
Speaker 2: solving skills too.
Speaker 1: That's great practical advice for the resume. Concrete examples impact projects.
Speaker 1: What about the behavioral side? Then those tell me about
Speaker 1: a time when questions how do you handle those?
Speaker 2: Well? Yeah, this can feel awkward. The trick is to
Speaker 2: have stories ready, but frame them to show specific qualities
Speaker 2: things companies look for, like initiative or leadership, teamwork, empathy,
Speaker 2: and don't just say what you did the book suggest
Speaker 2: breaking down the action part. So instead of just I
Speaker 2: solve the conflict, maybe say, well, I did three things. First,
Speaker 2: I talked to each person individually to understand their perspective. Second,
Speaker 2: I brought them together to find common ground. Third, we
Speaker 2: agreed on a new process. See how that gives more depth,
Speaker 2: shows your thought process. And yeah, hobbies can work too
Speaker 2: if you can frame them right. Ran a marathon shows discipline.
Speaker 2: Let a team in an online game shows leadership. Coordination
Speaker 2: connected back to positive traits.
Speaker 1: Makes sense. Frame everything to showcase relevant qualities.
Speaker 2: Okay, last big piece the offer negotiation. This is often
Speaker 2: the final step, but man, it can have a huge
Speaker 2: impact on your career. It really can. And where people
Speaker 2: sometimes leave value on the table, maybe because they're uncomfortable
Speaker 2: negotiating key things. The book mentions, First, understand the whole package.
Speaker 2: Don't just look at salary stock options. Amortize them maybe
Speaker 2: over three years, to compare offers fairly. Look at bonuses.
Speaker 2: Signing bonus benefits four to one K match everything. Second,
Speaker 2: have a viable alternative. Seriously, having another offer gives you
Speaker 2: so much more leverage. It makes your position much stronger. Third,
Speaker 2: have a specific ask. Don't just say I want more
Speaker 2: research what's reasonable for the role and your experience level,
Speaker 2: and ask for something specific eight thousand dollars more in
Speaker 2: salary or an extra five hundred stock units. It's much
Speaker 2: more effective. Fourth, it's okay to overshoot a little negotiation
Speaker 2: is usually a bit of give and take. Start a
Speaker 2: bit higher than your absolute minimum target, but be ready
Speaker 2: to justify it and maybe meet in the middle. And finally,
Speaker 2: maybe most importantly, think long term career development happiness. Don't
Speaker 2: just focus on the money right now. Consider the company's reputation,
Speaker 2: what you'll learn, promotion opportunities, the team, your manager, the
Speaker 2: product itself. Is it exciting? Does it align with your goals?
Speaker 2: These factors often matter way more for your long term
Speaker 2: career and satisfaction than just a few extra dollars up front.
Speaker 2: It's about the bigger picture.
Speaker 1: Wow, what an incredible deep dive. We've really covered the spectrum,
Speaker 1: haven't we From the nitty gritty of data structures and algorithms,
Speaker 1: to the strategy of walking through problems, analyzing efficiency with bigo,
Speaker 1: and then all the way to crafting your resume, handling
Speaker 1: behavioral questions, and negotiating that final offer thoughtfully. It seems
Speaker 1: like the real takeaway, like the book emphasizes, isn't just
Speaker 1: knowing this stuff but applying it rigorously, strategically exactly.
Speaker 2: And you know, this brings up kind of an interesting
Speaker 2: final thought. How am I really cultivating these disciplined ways
Speaker 2: of thinking, breaking down technical problems, optimizing managing your career strategically?
Speaker 2: How might that change things beyond just the job search? Like,
Speaker 2: how could it impact your ability to tackle any complex challenge,
Speaker 2: personal projects, big life decisions, maybe even how you learn
Speaker 2: new things? The underlying skills and they seem pretty badly applicable,
Speaker 2: don't they something to think about?
Speaker 1: That's a powerful question to leave everyone with. How far
Speaker 1: can these skills take you? Definitely food for thought. Well,
Speaker 1: thank you so much for joining us on this deep dive,
Speaker 1: Keep learning, keep exploring those connections, and we'll catch you
Speaker 1: on the next one.