Forem: Firecode.io🔥

Why the Leetcode Grind is Broken (And What Actually Works)

Firecode.io🔥 — Mon, 11 Aug 2025 00:00:13 +0000

Last week, I got a message that stopped me in my tracks. It was from a senior engineer who’d been grinding for half a year, someone who’d done everything “right” according to conventional wisdom.

I solved 847 Leetcode problems. Filtered by company. Memorized patterns. Six months of prep. Still bombed my Google onsite.

Sound familiar? If you’ve been through the interview gauntlet, you’ve probably felt this exact frustration. The sinking feeling when months of preparation evaporate the moment you’re standing at that whiteboard.

This engineer isn’t alone. They’re stuck in what I call the Leetcode Paradox : The more problems you solve, the less you actually remember. And ironically, the worse you perform when it matters most. It’s a cruel joke that the harder you work using the traditional approach, the more likely you are to fail when it counts.

The $1.6 Million Realization

Before I explain why the traditional grind is fundamentally broken, let me share something that might surprise you. The highest offer we’ve seen from a Firecode.io user? $1.6 million in total compensation for a staff+ engineer position.

This engineer didn’t solve 1000 problems. They didn’t memorize every possible tree traversal. They did something radically different — they learned how to actually learn. And that’s a lesson I learned the hard way myself, which ultimately led me to build Firecode.io.

The Dirty Secret Nobody Talks About

Here’s what happens during the typical interview prep journey:

Week 1 : You’re pumped. You knock out 20 easy problems. You feel like Neo seeing the Matrix for the first time.

Week 4 : You’ve moved to mediums. That confidence from week 1? Gone. Every problem feels like a completely new universe.

Week 8 : You’ve solved 200+ problems. But wait-what was that sliding window technique from week 2? How did that heap optimization work again? You know you’ve seen it, but the details are… fuzzy.

Week 12 : You’re solving problems by company tags, desperately hoping to get lucky. You’ve forgotten more than you remember. The interview is next week, and you’re cramming harder than you did for your algorithms final in college.

The Interview : They ask a variation of something you’ve definitely seen. Your mind goes blank. You know you know this, but under pressure, with someone watching, your brain refuses to connect the dots. You try to bluff your way through, but it’s obvious. You leave the interview feeling defeated, wondering where it all went wrong.

Why Your Brain is Working Against You

Let me introduce you to your worst enemy in interview prep: The Ebbinghaus Forgetting Curve.

Hermann Ebbinghaus discovered this in 1885 (yes, we’ve known about this for almost 140 years, yet we still prep the wrong way). Without reinforcement, you forget:

50% of new information within an hour
70% within 24 hours
90% within a week

That tree problem you solved three weeks ago? Your brain has already deleted 90% of it. There’s no recycling bin, no “recently deleted” folder in your mind. It’s gone. And the worst part? You won’t even realize it’s gone until you’re staring at a similar problem in an interview, desperately trying to remember that one clever insight that made everything click.

But here’s the kicker — when you re-solve a similar problem, you’re not actually learning. You’re just temporarily refreshing your cache. It feels productive, you get that dopamine hit when the solution passes all test cases, but it’s like writing in sand at high tide. The knowledge washes away just as quickly as it came, leaving you no better prepared than before.

The Leetcode Industrial Complex

Let’s be real for a second. The current system is broken by design. It’s not that Leetcode or similar platforms are bad — they’re incredible resources with thousands of quality problems. The issue is how we’re conditioned to use them, like trying to drink from a fire hose when what we really need is a carefully designed irrigation system:

The Random Walk Problem : Pick problems randomly or by company? Either way, you’re gambling with your time. You might solve 50 graph problems when your interview will focus on arrays.
The Memorization Trap : You’re not learning problem-solving; you’re memorizing solutions. When the interviewer throws a curveball (and they will), you’re toast.
The Confidence Death Spiral : Solve easy problems? “These are too simple.” Solve hard problems? “I’ll never get this in an interview.” There’s no winning.
The Forgetting Factory : By the time you’ve solved problem #500, you’ve forgotten the techniques from problems #1–100. You’re running on a treadmill that’s getting faster while you’re getting more tired.

What Actually Works: The Science Nobody Uses

After analyzing data and survey responses from 50,000+ engineers who’ve successfully landed jobs at Meta, Google, Nvidia, OpenAI, and other top companies, we confirmed something that we’ve always suspected:

The engineers who solved fewer problems but retained more knowledge consistently outperformed the grinders.

How? They tapped into something called spaced repetition -the learning technique that’s been proven by decades of cognitive science research but somehow never made it to interview prep.

Here’s the secret: Your brain doesn’t need more problems. It needs the right problems, at the right time, for the right number of times.

How I Discovered This Secret (The Hard Way)

Let me back up for a moment. I know exactly how frustrating the traditional approach is because I’ve been there. I graduated from Georgia Tech with a 4.0 GPA — sounds impressive, right? Except I picked the wrong major. Computer Engineering instead of Computer Science. While my CS friends were learning algorithms and data structures, I was knee-deep in circuit design and assembly language.

I had to teach myself to code. And when I started interviewing, I failed. Hard. Google? Rejected. Facebook? Didn’t make it past the phone screen. I was that guy solving hundreds of problems, thinking more was better. I’d stay up until 3 AM grinding Leetcode, only to freeze when faced with a simple variation in the actual interview.

Then I discovered spaced repetition — not through some eureka moment, but through desperation and experimentation. I started tracking which problems I could actually solve weeks later versus which ones I’d completely forgotten. The pattern was clear: the problems I’d seen multiple times at increasing intervals stuck; everything else vanished. I built a crude system using spreadsheets and calendar reminders, and something magical happened. The concepts started clicking. The patterns became intuitive rather than memorized.

Using this approach, I landed multiple offers from Google, Facebook, Twitter, and others. I ended up taking the Twitter offer, and since then, I’ve been on the other side of the table — participating in over 500 software engineering interview panels. I’ve seen brilliant engineers fail because they prepared wrong, and average engineers succeed because they prepared smart.

That’s why we built Firecode.io — to give every engineer access to the system that actually works.

The Firecode.io Difference: Learning That Actually Sticks

We built Firecode.io on a simple but radical premise: What if we applied actual learning science to coding interviews?

Instead of throwing you into an ocean of problems, we:

Intelligently sequence your learning : You see the right problem at the exact moment your brain is about to forget the concept. This isn’t random-it’s based on the SM2 algorithm, enhanced with proprietary modifications from analyzing millions of learning sessions.
Build, don’t memorize : Each problem reinforces multiple concepts. You’re not learning 1000 isolated solutions; you’re building a interconnected web of knowledge.
Test what matters : We use 1500+ real interview questions from actual tech companies. Not leetcode randoms-the actual problems being asked right now.
Make it sustainable : 20 minutes a day beats 6-hour weekend grinds. Consistency trumps intensity, every single time.

The Proof is in the Paychecks

Remember that $1.6M offer I mentioned? That engineer used Firecode.io for just 15–30 minutes daily for 4 months. No grinding. No burnout. Just consistent, scientifically-optimized practice. They told me later that the biggest difference wasn’t just landing the offer — it was the confidence they felt walking into each interview, knowing the knowledge was truly theirs, not temporarily borrowed from a solution they’d memorized.

But it’s not just about the outliers:

Average salary increase: $67,000
Success rate for onsite interviews: 64% (industry average: ~25%)
Average daily practice time: 22 minutes
Total unique problems solved: ~173 (not 3000!)

The Choice That Changes Everything

Look, I get it. The grind feels productive. There’s something satisfying about that Leetcode submission count going up, watching your profile turn green with daily submissions. It’s gamification at its finest, and I fell for it too. But if you’re reading this, some part of you knows it’s not working. You feel it in that moment of panic when you can’t remember a pattern you know you’ve seen before.

Here’s the truth: You don’t need to solve 1000 problems. You need to solve the right 150 problems in the right order at the right time. You need to learn, not memorize. You need a system that works with your brain, not against it.

Join 50,000+ Engineers Who’ve Cracked the Code

Firecode.io isn’t just another coding platform. It’s a learning engine built on decades of memory science, designed by engineers who’ve been exactly where you are.

Our users have landed jobs at:

Not through grinding. Through learning that actually sticks.

Your Interview Success Starts Today

Every week you wait is another week of inefficient prep. Another week closer to your interview without real preparation. Another week watching others land the jobs you want.

But it doesn’t have to be this way.

Firecode.io gives you:

🎯 1500+ real interview questions from top tech companies
🧠 Proprietary spaced repetition engine that ensures you remember what you learn
⚡ Just 20 minutes a day to stay interview-ready
📈 Personalized learning paths based on your target role and companies
🎮 Gamified experience that makes prep actually enjoyable

Start Free. See Results in Days.

Your next interview could change your life. The question is: Will you be ready?

Start your risk-free Firecode.io trial now →

See why engineers at Google, Meta, and Netflix choose Firecode.io to stay interview-ready.

Your future self (and your future $500K+ salary) will thank you.

P.S. Still skeptical? Try this experiment: Pick any algorithm you learned 3 weeks ago and implement it from scratch. No peeking. If you can’t, you’re not learning — you’re just temporarily memorizing. And that’s exactly why you need a better system. Trust me, I’ve been on both sides of this equation, and I know which one works.

Originally published at https://firecode.io on August 11, 2025.

Daily Coding Challenge: Height of a Binary Tree

Firecode.io🔥 — Sun, 10 Aug 2025 12:29:46 +0000

Picture yourself in a technical interview at LinkedIn. The interviewer sketches a binary tree on the whiteboard and asks, "How would you find its height?" This seemingly simple question is actually testing several important concepts: your understanding of tree structures, recursive thinking, and edge case handling. It's a classic warm-up problem that I've seen countless candidates either ace or stumble on, depending on their preparation.

Why This Problem Matters

The height of a binary tree problem appears frequently in technical interviews because it tests fundamental skills that transfer to more complex tree problems. Companies like LinkedIn, Microsoft, and Google often start with this question before moving to harder tree manipulation problems. Master this, and you'll have a solid foundation for tackling more advanced tree algorithms.

Binary Trees: A Quick Primer

Before we dive into the solution, let's refresh our understanding of binary trees. A binary tree is a hierarchical data structure where each node has at most two children, typically called the left and right child.

Key concepts to remember:

Root: The topmost node (node 1 in our example)
Leaf: A node with no children (nodes 4 and 5)
Height: The number of nodes on the longest path from root to leaf
Depth: The distance from the root to a specific node

Think of a binary tree like a family tree or organizational chart - there's a clear hierarchy, and we can trace paths from the top to any member.

Understanding the Problem

Let's break down what we're being asked to do:

Given: A root node of a binary tree

Find: The height of the tree

Important: An empty tree has height 0, a single node has height 1

Here's an example from our problem:

The longest paths (1 -> 3 -> 4) and (1 -> 3 -> 5) both have 3 nodes, so Height = 3

The key insight here is that the height is determined by the longest path from root to any leaf. In this case, we have two paths of length 3: 1 -> 3 -> 4 and 1 -> 3 -> 5.

Edge Cases to Consider

Empty tree (null root): Height = 0
Single node: Height = 1
Skewed tree (all nodes on one side): Height equals number of nodes
Balanced tree: Height is logarithmic to the number of nodes

Solution Approach

Let's think about this problem intuitively. If I asked you to find the tallest person in a group, you'd compare everyone's height and pick the maximum. Finding tree height works similarly - we need to find the maximum depth among all paths.

Building Intuition

Imagine you're standing at the root of the tree. To find the height:

Ask your left child: "What's your height?"
Ask your right child: "What's your height?"
Your height = 1 + max(left child's height, right child's height)

This naturally leads us to a recursive solution!

The Recursive Insight

The height of a binary tree is the number of nodes in its longest path from the root to its deepest leaf node. Talking recursively, when at the root node, the height of the binary tree is 1 + the maximum of the height of the left and right subtrees.

Pro tip: When solving tree problems, always think recursively first. Trees have a natural recursive structure that makes recursive solutions elegant and intuitive.

Implementation

Here's our solution in Java:

public class Solution {
  public int getHeight(TreeNode root) {
    // Base case: empty tree has height 0
    if (root == null) {
      return 0;
    }

    // Recursive case: height is 1 (current node) + max height of subtrees
    return 1 + Math.max(getHeight(root.left), getHeight(root.right));
  }
}

Let's trace through our example to see how this works:

(See the full animation at https://firecode.io/firelogs/problems/31/height-of-a-binary-tree/)

Step-by-step execution:
1. Start at root (1): getHeight(1)
2. Visit left child (2): getHeight(2) returns 1 (no children)
3. Visit right child (3): getHeight(3)
4. Visit left child of 3 (4): getHeight(4) returns 1 (no children)
5. Visit right child of 3 (5): getHeight(5) returns 1 (no children)
6. Node 3 returns: 1 + max(1, 1) = 2
7. Node 1 returns: 1 + max(1, 2) = 3

The beauty of this solution lies in its simplicity. Each node only needs to know the height of its subtrees to calculate its own contribution to the overall height.

Complexity Analysis

Time Complexity: O(n)

We visit each node exactly once to compute its height. Whether the tree is balanced or skewed, we must examine every node to ensure we don't miss the longest path. Therefore, the time complexity is O(n), where n is the number of nodes.

Space Complexity: O(log n) average, O(n) worst case

The space complexity comes from the recursive call stack:

Best case (balanced tree): O(log n) - The recursion depth equals the tree height
Worst case (skewed tree): O(n) - When all nodes form a single path

I've seen many candidates forget to mention the space complexity of recursion. Remember: recursive calls use stack space!

Common Pitfalls

Having interviewed hundreds of candidates, here are the most common mistakes I see:

Off-by-one errors: Forgetting that a single node has height 1, not 0
Null handling: Not properly handling the empty tree case
Confusing height with depth: Height counts nodes, depth counts edges
Non-recursive attempts: While possible iteratively, it's much more complex

Pro tip: Always clarify the definition of height with your interviewer. Some sources define it as the number of edges (not nodes) on the longest path.

Interview Tips

When solving this problem in an interview:

Start with clarifying questions:
- "Should I count nodes or edges for height?"
- "What should I return for an empty tree?"
- "Can I assume the TreeNode class is already defined?"
Talk through your approach:
- "I'll use recursion since trees have a recursive structure"
- "My base case will handle null nodes"
- "I'll recursively find the height of left and right subtrees"

Remember, consistent practice is the key to succeeding in coding interviews. This problem and thousands of others are available on Firecode.io, where over 50,000 users have successfully prepared for technical interviews and landed six and seven-figure jobs at top tech companies. Whether you’re just starting or preparing for your dream job, mastering fundamentals like this will set you up for success.

Happy coding, and may all your trees be perfectly balanced! 🌳

Consider follow-up questions:
- "How would you find the height iteratively?" (Use level-order traversal)
- "What if we need to find height frequently?" (Consider caching)
- "How would you find the diameter of the tree?" (Related problem)
If you get stuck:
- Draw a small example (3-4 nodes)
- Think about the base case first
- Remember that tree problems often have elegant recursive solutions

Key Takeaway

The height of a binary tree problem teaches us a fundamental pattern in tree algorithms: combine results from subtrees to solve for the current node. This pattern appears in countless tree problems - from calculating sums to checking balance to finding paths. Master this recursive thinking, and you'll find many tree problems become much more approachable.

Practice Makes Perfect

This problem is just the beginning of your tree algorithm journey. Once you're comfortable with finding height, try these related problems to deepen your understanding:

Find the minimum depth of a binary tree
Check if a binary tree is balanced
Find the diameter of a binary tree

Remember, consistent practice is the key to succeeding in coding interviews. This problem and thousands of others are available on Firecode.io, where over 50,000 users have successfully prepared for technical interviews and landed six and seven-figure jobs at top tech companies. Whether you're just starting or preparing for your dream job, mastering fundamentals like this will set you up for success.

Happy coding, and may all your trees be perfectly balanced! 🌳